nep-mac New Economics Papers
on Macroeconomics
Issue of 2016‒09‒04
eighty-two papers chosen by
Soumitra K Mallick
Indian Institute of Social Welfare and Business Management

  1. Macroeconomic Modeling of Financial Frictions for Macroprudential Policymaking : A Review of Pressing Challenges By Michael T. Kiley
  2. The Effect of Unconventional Fiscal Policy on Consumption Expenditure By Francesco D’Acunto; Daniel Hoang; Michael Weber
  3. Transitional Dynamics and Long-run Optimal Taxation Under Incomplete Markets By Acikgoz, Omer
  4. Low Inflation in the United States : A Summary of Recent Research By Michael T. Kiley
  5. Will Household Expectations Follow Professional Forecasters'? By Ekaterina V. Peneva; Daeus Jorento; Emily Massaro
  6. Leaning Within a Flexible Inflation-Targeting Framework: Review of Costs and Benefits By Denis Gorea; Oleksiy Kryvtsov; Tamon Takamura
  7. Macroeconomic Sources of Recent Interest Rate Fluctuations By Stefania D'Amico; Thomas B. King; Min Wei
  8. Dynamics of Overnight Money Markets : What Has Changed at the Zero Lower Bound? By Elizabeth C. Klee; Zeynep Senyuz; Emre Yoldas
  9. Mining and Energy Boom, Dutch Disease and Informality in Colombia: a DSGE Approach By Carlos Ballesteros
  10. Reflections on the natural rate of interest, its measurement, monetary policy and the zero lower bound By Cukierman, Alex
  11. New Methods for Macro-Financial Model Comparison and Policy Analysis By Afanasyeva, Elena; Kuete, Meguy; Wieland, Volker; Yoo, Jinhyuk
  12. Nominal rigidities in debt and product markets By Garriga, Carlos; Kydland, Finn E.; Sustek, Roman
  13. Nominal rigidities in debt and product markets By Carlos Garriga; Finn E. Kydland; Roman Sustek
  14. Negative interest rate policies: Sources and implications By Carlos Arteta; M. Ayhan Kose; Marc Stocker; Temel Taskin
  15. Has the Inflation Risk Premium Fallen? Is it Now Negative? By Andrew Y. Chen; Eric Engstrom; Olesya V. Grishchenko
  16. Credibility of Optimal Forward Guidance at the Interest Rate Lower Bound By Taisuke Nakata
  17. Debt Statistics a la Carte : Alternative Recipes for Measuring Government Indebtedness By Daniel A. Dias; Mark L. J. Wright
  18. Financial Market Imperfections and Labour Market Outcomes By Alireza Sepahsalari
  19. The Expected Real Interest Rate in the Long Run : Time Series Evidence with the Effective Lower Bound By Benjamin K. Johannsen; Elmar Mertens
  20. Recession Risk and the Excess Bond Premium By Giovanni Favara; Simon Gilchrist; Kurt F. Lewis; Egon Zakrajsek
  21. Risk shocks close to the zero lower bound By Seneca, Martin
  22. Durations at the Zero Lower Bound By Richard Dennis
  23. Did the West Coast Port Dispute Contribute to the First-Quarter GDP Slowdown? By Mary Amiti; Tyler Bodine-Smith; Michele Cavallo; Logan T. Lewis
  24. Did the Fed's Announcement of an Inflation Objective Influence Expectations? By Alan K. Detmeister; Daeus Jorento; Emily Massaro; Ekaterina V. Peneva
  25. Sustainable International Monetary Policy Cooperation By Ippei Fujiwara; Timothy Kam; Takeki Sunakawa
  26. The Risk of Returning to the Effective Lower Bound : An Implication for Inflation Dynamics after Lift-Off By Timothy S. Hills; Taisuke Nakata; Sebastian Schmidt
  27. The Economic Consequences of the 1953 London Debt Agreement By Gregori Galofré-Vilà; Martin McKee; Christopher M. Meissner; David Stuckler
  28. Forecasts of Economic Activity in the Great Recession By Claudia R. Sahm
  29. The Social Discount Rate in Developing Countries By Missaka Warusawitharana
  30. Monetary Policy and Asset Valuation: Evidence From a Markov-Switching cay By Francesco Bianchi; Martin Lettau; Sydney C. Ludvigson
  31. Qualitative Guidance and Predictability of Monetary Policy in South Africa By Alain Kabundi; NtuthukoTsokodibane
  32. Quadrilemma not Trilemma: Fiscal Policy Matters By Hao Jin
  33. What Caused the Great Recession in the Eurozone? By Hetzel, Robert L.
  34. Consumer Spending and Fiscal Consolidation: Evidence from a Housing Tax Experiment By Surico, P.; Trezzi, R.
  35. The Federal Reserve's Overnight and Term Reverse Repurchase Agreement Operations in the Financial Accounts of the United States By Ralf R. Meisenzahl
  36. Toward Removal of the Swiss Franc Cap: Market Expectations and Verbal Interventions By Nikola Mirkov; Igor Pozdeev; Paul Söderlind
  37. Financial market volatility, macroeconomic fundamentals and investor sentiment By Chiu, Ching-Wai (Jeremy); Harris, Richard; Stoja, Evarist; Chin, Michael
  38. The Surprising Strength of U.S. Imports During the Recovery By Michele Cavallo
  39. ¿Es la Argentina el país donde se cumple el trilema monetario? By Zarate, Cristina A.
  40. Las políticas del Banco de la República durante un auge entre dos crisis, 1930-1951. By Juliana Jaramillo-Echeverri.; Adolfo Meisel-Roca.
  41. Household Borrowing Constraints and Residential Investment Dynamics and Resource Rents in Developing Countries By Hashmat U. Khan; Jean-François Rouillard
  42. Monetary versus macroprudential policies causal impacts of interest rates and credit controls in the era of the UK Radcliffe Report By Aikman, David; Bush, Oliver; Davis, Alan
  43. Managing Heterogeneous and Unanchored Expectations: A Monetary Policy Analysis By Hommes, C.H.; Lustenhouwer, J.
  44. Where do I see the Monetary Policy Normalization Tools on the Fed's Balance Sheet and Income Statement? By Christian Miller; Casey Clark
  45. November 2014 Update of the FRB/US Model By Jean-Philippe Laforte; John M. Roberts
  46. The Recent Decline in Long-Term Unemployment By David M. Byrne; Eugenio P. Pinto
  47. Annual Data on Investment and Capital Stocks By Christopher J. Kurz; Norman J. Morin
  48. The Relationship Between Oil Prices and Inflation Compensation By Alejandro Perez-Segura; Robert J. Vigfusson
  49. Financial Safety Nets By Bengui, Julien; Bianchi, Javier; Coulibaly, Louphou
  50. 货币增速剪刀差与股票市场收益率的时变格兰杰因果关系研究 By Cai, Yifei
  51. Monetary Policy, Investment, and Firm Heterogeneity By Thomas Winberry; Pablo Ottonello
  52. Reconstruction multipliers By Trezzi, R.; Porcelli, F.
  53. A Study on Impact of Foreign Direct Investment on Gross Domestic Production in India By Tamilselvan, M.; Manikandan, S.
  54. Financing poverty eradication By Anis Chowdhury
  55. The foreign currency mortgage loans in the Polish banking sector and its possible macroeconomic and political consequences By Krzysztof Czerkas
  56. Human Capital, Public Debt, and Economic Growth: A Political Economy Analysis By Tetsuo Ono; Yuki Uchida
  57. Step away from the zero lower bound: Small open economies in a world of secular stagnation By Corsetti, G.; Mavroeidi, E.; Thwaites, G.; Wolf, M.
  58. The Political Economy of Debt and Entitlements By Laurent Bouton; Alessandro Lizzeri; Nicola Persico
  59. Does the Unemployment Invariance Hypothesis Hold for Canada? By Tansel, Aysit; Ozdemir, Zeynel Abidin; Aksoy, Emre
  60. The Political Economy of Debt and Entitlements By Bouton, Laurent; Lizzeri, Alessandro; Persico, Nicola
  61. Social Capital, Trust and Well-being in the Evaluation of Wealth By Kirk Hamilton; John F. Helliwell; Michael Woolcock
  62. Discussion of “Evaluating Monetary Policy Operational Frameworks” by Ulrich Bindseil: remarks at the 2016 Economic Policy Symposium at Jackson Hole, Wyoming By Potter, Simon M.
  63. Sovereign Debt Restructuring After Argentina By Porzecanski, Arturo C.
  64. Hours Worked in Europe and the US: New Data, New Answers By Bick, Alexander; Brüggemann, Bettina; Fuchs-Schündeln, Nicola
  65. The FOMC Meeting Minutes : An Assessment of Counting Words and the Diversity of Views By Ellen E. Meade; Nicholas A. Burk; Melanie Josselyn
  66. The Impact of Earthquakes on Economic Activity: Evidence from Italy By Porcelli, F.; Trezzi, R.
  67. Assessing the effects of unconventional monetary policy on pension funds risk incentives By Boubaker, Sabri; Gounopoulos, Dimitrios; Nguyen, Duc Khuong; Paltalidis, Nikos
  68. Macroeconomic tail events with non-linear Bayesian VARs By Chiu, Ching-Wai (Jeremy); Hacioglu Hoke, Sinem
  69. Low long-term interest rates as a global phenomenon By Peter Hördahl; Jhuvesh Sobrun; Philip Turner
  70. Public Debt and Private Firm Funding: Evidence from Chinese Cities By Yi Huang; Marco Pagano; Ug Panizza; Tano Santos
  71. Análisis experimental de la evasión en Colombia By Ana Paola Cruz Rodríguez; Alvin Alejandro Olarte
  72. Bayesian nonparametric sparse seemingly unrelated regression model (SUR) By Monica Billio; Roberto Casarin; Luca Rossini
  73. Causal Change Detection in Possibly Integrated Systems: Revisiting the Money-Income Relationship By Shuping Shi; Stan Hurn; Peter C B Phillips
  74. Financialisation in emerging economies: a systematic overview and comparison with Anglo-Saxon economies By Ewa Karwowski; Engelbert Stockhammer
  75. Potential Output and Recessions : Are We Fooling Ourselves? By Robert F. Martin; Tenyanna Munyan; Beth Anne Wilson
  76. Estimating the effects of global uncertainty in open economies By Silvia Delrio
  77. The Fight-or-Flight Response to the Joneses By Barnett, Richard; Bhattacharya, Joydeep; Bunzel, Helle
  78. Social security wealth and household asset holdings: new evidence from Belgium. By Mathieu Lefebvre; Sergio Perelman
  79. The Origins of Argentina’s Litigation and Arbitration Saga, 2002-2016 By Porzecanski, Arturo C.
  80. Insurance-markets Equilibrium with Sequential Non-convex Straight-time and Over-time Labor Supply By Vasilev, Aleksandar
  81. The inherent benefit of monetary unions By Groll, Dominik; Monacelli, Tommaso
  82. Los Conflictos Socio-Ambientales y el Valor de las Acciones de las Grandes Empresas Mineras en el Perú: Evaluando la Teoría de las Opciones Reales. By César Huaroto De la Cruz; Arturo Leonardo Vásquez Cordano

  1. By: Michael T. Kiley
    Abstract: FEDS Notes Print{{p}}May 26, 2016{{p}}Macroeconomic Modeling of Financial Frictions for Macroprudential Policymaking: A Review{{p}}of Pressing Challenges{{p}}Michael T. Kiley1{{p}}Structural macroeconomic modeling plays a central role economic policy discussions. Over the past fifty years, the overwhelming majority{{p}}of such efforts have focused on the structural features of household, firm, and government behavior that lead to cyclical fluctuations in{{p}}employment and inflation and the roles of monetary and fiscal policy in ameliorating undesirable volatility in economic performance. In{{p}}recent years, the potential role of macroprudential policies in limiting excessive volatility in the financial sector and the consequent effects{{p}}on economic performance has risen to the fore in academic and policy discussions. While progress in modeling for macroprudential policy{{p}}analysis has been substantial, there remain many important challenges, and consensus on a core modeling framework remains far away.{{p}}This note reviews some of the progress witnessed in recent years and challenges that remain.{{p}}The consensus modeling framework at central banks{{p}}At the Federal Reserve, macroeconomic modeling efforts since the 1960s led to the development of models that captured the central{{p}}views of macroeconomic thought during their time, including the development of the MPS model from the late 1960s through the early{{p}}1980s, the FRB/US model in the 1990s, and dynamic-stochastic-general-equilibrium (DSGE) models in the 2000s. While there has been{{p}}significant evolution in the structure of these models--and in particular in the modeling of household and firm expectations for future{{p}}income, inflation, and policymaker behavior, the core elements of all of these models are very similar. Nominal price and wage rigidities{{p}}contribute to inefficient volatility in economic activity. Household spending is determined by current income, expected future income, and{{p}}financial conditions, including household wealth and the term structure of interest rates. Spending on productive capital (investment) and{{p}}the employment decisions of firm are determined by current demand and assessments of the relative payoff to investments in capital vs.{{p}}returning a firm's earnings to its shareholders (as captured in neoclassical theories of investment in the "user-cost" and "Q" traditions). As{{p}}a result of these similarities, the channels through which monetary policy can contribute to economic stability are broadly similar across{{p}}modeling frameworks: Anchoring inflation expectations through a commitment to an inflation target lowers volatility in financial conditions{{p}}and hence in economic activity and inflation, while countercyclical adjustments in nominal interest rates lead to changes in household{{p}}wealth, the financing conditions facing firms, and the exchange value of the dollar that contribute to stability in employment and inflation.{{p}}The channels through which fiscal policy adjustments may stabilize economic performance are also broadly similar across these modeling{{p}}frameworks.2 Moreover, these common features and properties are also shared by models used at many central banks around the world.{{p}}Missing features{{p}}This high level review of modeling frameworks has not mentioned a number of central features of macroeconomic analysis. The notion{{p}}that maturity transformation by financial intermediaries leaves such institutions vulnerable to runs and that such runs may cause severe{{p}}contractions in economic activity, as observed in the Panic of 1907, the Great Depression, the recent global financial crisis, and scores of{{p}}other examples, has not traditionally been an element of macroeconomic models. Neither has the idea that large, unexpected losses on{{p}}assets held by financial institutions may lead them to reduce their willingness to intermediate, contributing to a decline in asset valuations{{p}}via fire sales, a contraction in economic activity, and low inflation; nor has the possibility that constraints on household leverage{{p}}associated with mortgage borrowing and swings in real estate valuations may lead to a sharp contraction in household spending should{{p}}such leverage constraints bind severely.{{p}}Macroeconomic models are necessarily simplifications of reality, and the omission of these factors is understandable (as many of these{{p}}factors arguably played only a small role in economic fluctuations in the decades prior to the recent financial crisis). And some key policy{{p}}implications of macroeconomic models have, at least in research so far, proven largely robust to the inclusion of these considerations: For{{p}}example, the central role of price stability in defining good monetary policy within DSGE models is shared by many such models when{{p}}leverage constraints on intermediaries or households are incorporated.3 But it is clear that consideration of these issues is important when{{p}}thinking about macroprudential policy questions such as{{p}}􀁺􇩔 To what degree have higher capital requirements for banks (such as those associated with Basel 3) increased the resilience of the{{p}}financial sector and contributed to greater economic stability going forward?{{p}}􀁺􇩈 How might cyclical adjustments in macroprudential instruments such as the countercyclical capital buffer or maximum loan-to-value{{p}}ratios on mortgage loans mitigate undesirable boom/bust credit cycles?{{p}}􀁺􇩈 How do the higher capital and liquidity requirements for financial intermediaries interact over the business cycle? To what extent{{p}}are such regulations complements (or substitutes)?4{{p}}Moreover, addressing such questions requires moving beyond the now-standard incorporation of frictions in nonfinancial firm financing{{p}}conditions via simple financial-accelerator mechanisms.5{{p}}Progress to date{{p}}Incorporating bank leverage{{p}}DSGE models with leveraged financial intermediaries have been considered in a large number of recent academic and central bank{{p}}research studies. Many of these analyses share a number of features: The leverage of financial intermediaries is limited through some{{p}}combination of regulatory constraints or market discipline (where the latter typically is some form of participation constraint imposed by{{p}}investors in bank debt as part of the debt contract between banks and investors); equity is more costly than debt and these leverage{{p}} FRB: FEDS Notes: Macroeconomic Modeling of Financial Frictions for Macroprudential ... Page 1 of 5{{p}} 5/26/2016{{p}}constraints are binding at all times, with the latter assumption often made for analytical tractability; and intermediaries are essential in the{{p}}sense that households have limited ability to directly finance nonfinancial firms.6{{p}}Some very preliminary policy lessons have been drawn from such exercises. Owing to incentives of financial institutions to assume{{p}}excessive leverage related to implicit or explicit subsidies (related to taxes, potential bailouts), misalignment of incentives between{{p}}managers and shareholders, or pecuniary externalities through which intermediary leverage affects movements in market prices, capital{{p}}requirements substantially above pre-crisis norms likely contribute to economic welfare.7 While this result is reminiscent of the notion that{{p}}higher capital requirements are likely to reduce the probability of banking system crises, macroeconomic models typically do not directly{{p}}consider the possibility of such "nonlinear" events, reflecting both modeling conventions and computational considerations.8 For example,{{p}}macroeconomic models contemplating the role of bank capital requirements may include spillovers from distress at institutions on the{{p}}broader system through pecuniary externalities, but models typically abstract from considerations related to failure of large institutions on{{p}}other institutions through network linkages. On the computational front, it remains challenging to model the interaction of aspects of the{{p}}distribution of capital and risks across the financial system and macroeconomic risks, and occasionally-binding leverage constraints on{{p}}banks or households are often not considered.9{{p}}An exception to this characterization is the model of Brunnermeier and Sannikov (2014), who study the global dynamics of a stylized{{p}}model in which leveraged investments in productive capital lead to highly nonlinear equilibrium dynamics of two types. First, large shocks{{p}}that depress net worth of borrowers have much larger depressing effects on asset prices and investment than large shocks increasing net{{p}}worth; these dynamics highlight how incorporation of exogenous changes in the volatility of shocks in models approximated via{{p}}perturbation methods, as in some recent efforts to capture Great-Recession dynamics, are incomplete. Second, the global solution to the{{p}}model involves an endogenous distribution of borrower net worth in which a period of low volatility leads borrowers to become highly{{p}}levered, and this high leverage implies that even small adverse shocks can have large adverse consequences; in other words, the model{{p}}captures a stylized version of the idea that low exogenous risk may contribute to the buildup of endogenous risk, potentially leading to a{{p}}crisis. Incorporation of such dynamics into the types of DSGE models used for policy analysis has been limited, although efforts to pull{{p}}insights from the quantitative general-equilibrium literature on "sudden stops" captures some of these ideas and may provide quantitative{{p}}guidance for macroprudential modeling efforts.10{{p}}Turning to business cycle implications of more traditional DSGE models, higher capital requirements for intermediaries--within the range{{p}}considered in policy debates--have very modest effects on macroeconomic volatility; that is, the increase in banking sector resilience that{{p}}is associated with greater loss absorbing capacity has very little effect on the degree to which adjustments in bank leverage following{{p}}shocks amplify the macroeconomic consequences of such shocks in several models developed to date. This result is reminiscent of the{{p}}limited degree of endogenous amplification associated with financial accelerator mechanisms found in some previous studies, and it is not{{p}}clear whether capturing more nonlinear mechanisms would enhance amplification and the role of intermediary leverage in the{{p}}transmission of shocks in a manner more similar to the framework of Brunnermeier and Sannikov (2014). As a result, computational{{p}}advances related to the solution and estimation of models capturing potential nonlinearities, including (but not limited to) a number of{{p}}occasionally binding constraints, is a promising direction.11{{p}}Housing and the role of loan-to-value ratios{{p}}Incorporation of a leverage constraint in which households borrow against housing collateral has become common, most typically building{{p}}off the framework developed by Iacoviello (2005). An important recent strand of work analyzes the nonlinear effects of house prices in{{p}}such a framework. When homeowner wealth is high owing to high housing prices, the borrowing constraint facing borrowers is relatively{{p}}slack and such borrowers behave more like "permanent-income consumers". In contrast, a collapse in housing prices makes the{{p}}borrowing constraint bind tightly, limiting the ability of households to smooth consumption and making household spending more sensitive{{p}}to shocks. Guerrieri and Iacoviello (2014) estimate that these nonlinear effects may account for the very sharp decline in spending in the{{p}}U.S. Great Recession.{{p}}Modeling of such transmission channels seems especially important in light of evidence that loan-to-value ratios may be an effective{{p}}macroprudential tool with which to lean against housing cycles.12{{p}}Liquidity transformation{{p}}The emphasis in the modeling frameworks discussed so far has been on the potential for household or intermediary leverage to amplify{{p}}the transmission of shocks to macroeconomic activity and the role of regulations in shaping these responses. And the collapse in house{{p}}prices and the losses on intermediary balance sheets contributed to the contraction in credit supply and therefore were important factors{{p}}shaping to the Great Recession. But an equally important dynamic, at least in some narratives, was the "run" on the financial system (e.g.,{{p}}Gorton and Metrick, 2012). The economic mechanisms at play are familiar from Diamond and Dybvig (1983): Intermediaries issued{{p}}liabilities that were redeemable in the short run, but backed by long-term assets which--should a run occur--could only be sold at a{{p}}discount determined endogenously; in such conditions, a coordinated run by "depositors" may emerge as an equilibrium if conditions{{p}}support a sufficiently low fire-sale price for the intermediaries' assets.{{p}}To date, few DSGE models incorporate such mechanisms. One factor limiting progress has been the fact that the existence of a run{{p}}equilibrium often coincides with an equilibrium in which no run occurs, and typical DSGE computational techniques do not incorporate{{p}}potential switches between alternative equilibria as a source of financial and economic fluctuations.13 Gertler and Kiyotaki (2015) is one{{p}}example integrating a bank-run equilibrium into a DSGE model. However, their analysis is not of economic fluctuations, but rather focuses{{p}}primarily on the conditions that may allow a run as an equilibrium. This partial step forward does have some intuitive policy-related{{p}} payoffs: For example, a bank run equilibrium is less likely if capital at intermediaries is high, as in this case the ratio of runnable liabilities{{p}}to illiquid assets is lower and hence the equilibrium fire-sale price of such assets is higher, limiting the possibility of a run equilibrium.{{p}}Pulling together the pieces{{p}}This review has highlighted substantial progress. Several areas appear ripe for greater integration.{{p}}Many of the models discussed that analyze macroprudential issues abstract from price and wage rigidities. This abstraction creates{{p}} FRB: FEDS Notes: Macroeconomic Modeling of Financial Frictions for Macroprudential ... Page 2 of 5{{p}} 5/26/2016{{p}}challenges. First, the qualitative properties of models with and without nominal price and wage rigidities in response to shocks can differ{{p}}substantially from that of models with such nominal frictions; in certain cases, the responses of the economy to shocks can have opposite{{p}}signs depending on whether the model does or does not have nominal price rigidities, and a model's properties need not approach the{{p}}"flexible price" version as price rigidities approach zero.14 Moreover, such models cannot consider the interaction of monetary policy and{{p}}macroprudential policies.{{p}}More generally, many contributions focus on one set of financial frictions and do not consider their interaction or relative quantitative{{p}}importance. Guerrieri and Iacoviello (2015) consider an occasionally-binding constraint on household borrowing and the role of the{{p}}fluctuations in house prices; Kiley and Sim (2015) and Iacoviello (2015) model leverage constraint on intermediaries and the role of{{p}}shocks to this constraint in fluctuations in credit supply and economic activity; Lindé, Smets, and Wouters (2016) estimate a model with{{p}}financial accelerator mechanisms facing nonfinancial firms and changes in the volatility of shocks. Each of these frictions is plausible. Nut{{p}}the quantitative findings in such studies likely depend importantly on the fact that each study focuses on an individual friction: For{{p}}example, Guerrieri and Iacoviello (2014) attribute a large share of the decline in activity to the decline in house prices and the effects of{{p}}the zero-lower bound on nominal interest rates; Iacoviello (2015) attributes roughly equal shares of the decline in output during the Great{{p}}Recession to the losses born by banks and the effect of falling house, but does not consider the effect of the zero-lower bound because{{p}}the model has no nominal rigidities; Lindé, Smets, and Wouters (2016) attribute a large share of the decline to a tightening in the credit{{p}}frictions facing nonfinancial firms and the zero-lower bound. Such findings probably stem from each studies focus on one friction, and{{p}}seem unlikely to be robust to examination of multiple frictions within the same model.{{p}}Developing such complicated models will be difficult, as computational issues arise and the data may not be especially informative about{{p}}key parameters. For this reason, macroprudential policy discussions may be increasingly informed by macroeconomic models in coming{{p}}years, but microeconomic analyses of the distortions in individual markets and the policy actions that can mitigate such distortions will{{p}}likely remain central.{{p}}References{{p}}Akinci, Ozge and Ryan Chahrour, 2015. "Good News Is Bad News: Leverage Cycles and Sudden Stops." Federal Reserve Bank of New{{p}}York Staff Reports, no. 738, September.{{p}}Akinci, Ozge and Jane Olmstead-Rumsey, 2015. "How Effective are Macroprudential Policies? An Empirical Investigation." Board of{{p}}Governors of the Federal Reserve System, International Finance Discussion Papers Number 1136, May.{{p}}Angelini, Paolo & Laurent Clerc & Vasco Cúrdia & Leonardo Gambacorta & Andrea Gerali & Alberto Locarno & Roberto Motto & Werner{{p}}Roeger & Skander Van den Heuvel & Jan Vlcek, 2015. "Basel III: Long-term Impact on Economic Performance and Fluctuations,"{{p}}Manchester School, University of Manchester, vol. 83(2), pages 217-251, 03.{{p}}Begenau, Juliane. "Capital Requirements, Risk Choice, and Liquidity Provision in a Business Cycle Model." Harvard Business School{{p}}Working Paper, No. 15-072, March 2015. (Revised November 2015.){{p}}Bernanke, Ben S. & Gertler, Mark & Gilchrist, Simon, 1999. "The financial accelerator in a quantitative business cycle framework," in: J. B.{{p}}Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 21, pages 1341-1393.Elsevier.{{p}}Blanchard, Olivier, 2014. Where Danger Lurks. Finance & Development, September, Vol. 51, No. 3.{{p}}Blanchard, Olivier, 2015. "Rethinking Macro Policy: Progress or Confusion. Chapter 28 in Progress and Confusion: The State of{{p}}Macroeconomic Policy (MIT Press, 2016).{{p}}Boivin, Jean & Kiley, Michael T. & Mishkin, Frederic S., 2010. "How Has the Monetary Transmission Mechanism Evolved Over Time?," in:{{p}}Benjamin M. Friedman & Michael Woodford (ed.), Handbook of Monetary Economics, edition 1, volume 3, chapter 8, pages 369-422,{{p}}Elsevier.{{p}}Brayton, Flint, Thomas Laubach, and David Reifschneider, 2014. "The FRB/US Model: A Tool for Macroeconomic Policy Analysis," FEDS{{p}}Notes, April.{{p}}Brayton, Flint and Eileen Mauskopf, 1985. ''The Federal Reserve Board MPS Quarterly Econometric Model of the U.S. Economy,''{{p}}Economic Modelling, vol. 3 (July), pp. 170-292.{{p}}Coenen, Günter, Christopher J. 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"Securitized banking and the run on repo," Journal of Financial Economics, Elsevier, vol. 104(3),{{p}}pages 425-451.{{p}}Guerrieri, Luca, and Matteo Iacoviello, 2014. "Collateral Constraints and Macroeconomic Asymmetries," International Finance Discussion{{p}}Papers 2013-1082, Board of Governors of the Federal Reserve System.{{p}}Guerrieri, Luca, and Matteo Iacoviello, 2015. "Occbin: A Toolkit to Solve Models with Occasionally Binding Constraints Easily," Journal of{{p}}Monetary Economics, March, vol. 70, pp. 22-38.{{p}}Guerrieri, Luca & Iacoviello, Matteo & Covas, Francisco & Driscoll, John C. & Kiley, Michael T. & Jahan-Parvar, Mohammad & Queraltó,{{p}}Albert & Sim, Jae W., 2015. "Macroeconomic Effects of Banking Sector Losses across Structural Models," Finance and Economics{{p}}Discussion Series 2015-44, Board of Governors of the Federal Reserve System (U.S.).{{p}}Iacoviello, Matteo, 2005. "House Prices, Borrowing Constraints, and Monetary Policy in the Business Cycle," American Economic Review,{{p}}American Economic Association, vol. 95(3), pages 739-764, June.{{p}}Iacoviello, Matteo, 2015. "Financial Business Cycles," Review of Economic Dynamics, vol 18(1), pp.140–163.{{p}}Kara, Gazi Ishak, and S. Mehmet Ozsoy (2016). "Bank regulation under fire sale externalities (PDF)," Finance and Economics Discussion{{p}}Series 2016-026. Washington: Board of Governors of the Federal Reserve System.{{p}}Kiley, Michael T., 2013. "Output gaps," Journal of Macroeconomics, Elsevier, vol. 37(C), pages 1-18 (2013).{{p}}Kiley, Michael T. & Sim, Jae W., 2014. "Bank capital and the macroeconomy: Policy considerations," Journal of Economic Dynamics and{{p}}Control, Elsevier, vol. 43(C), pages 175-198.{{p}}Kiley, Michael T. & Sim, Jae W., 2015. "Optimal Monetary and Macroprudential Policies: Gains and Pitfalls in a Model of Financial{{p}}Intermediation," Finance and Economics Discussion Series 2015-78, Board of Governors of the Federal Reserve System (U.S.).{{p}}Kocherlakota, Narayana, 2016. "Fragility of Purely Real Macroeconomic Models," NBER Working Papers 21866, National Bureau of{{p}}Economic Research, Inc.{{p}}Lindé, Jesper & Frank Smets & Rafael Wouters, 2016. "Challenges for Central Banks' Macro Models". Mimeo, Sveriges Riksbank, April.{{p}}Maliar, Lilia and Serguei Maliar, 2015, "Merging simulation and projection approaches to solve high-dimensional problems with an{{p}}application to a new Keynesian model" Quantitative Economics 6/1, pages 1-47{{p}}Malin, Benjamin, Dirk Krueger, and Felix Kubler, 2007. "Computing Stochastic Dynamic Economic Models with a Large Number of State{{p}} Variables: A Description and Application of a Smolyak-Collocation Method." NBER Working Paper No. 13517, October.{{p}}1. The author is Senior Associate Director, Division of Financial Stability, and Senior Adviser, Division of Research and Statistics, at the Federal Reserve Board.{{p}} Email: The views expressed herein are those of the author, and do not reflect those of the Federal Reserve or its staff. Return to text{{p}}2. For descriptions of related models and their similarities, see Brayton and Mauskopf (1985), Erceg, Guerrieri, and Gust (2006), Boivin, Kiley, and Mishkin{{p}}(2010), Coenen et al (2012), Kiley (2013), and Brayton, Laubach, and Reifschneider (2014). Return to text{{p}}3. For example, Iacoviello (2005) presents a model with a leverage constraint on mortgage borrowing; Kiley and Sim (2015) present a model with intermediation.{{p}}Both models imply that good monetary policy focuses on price stability. Return to text{{p}}4. Kara and Oszoy (2016) consider this question is a simple, finite-period model. Return to text{{p}}5. Such financial accelerator mechanisms (e.g., Bernanke, Gertler, and Gilchrist, 1999) have now become standard in New-Keynesian models of the type used{{p}}by central banks. While such models have been helpful in incorporating and understanding the role of credit spreads in cyclical fluctuations, existing{{p}}implementations do not appear to alter significant the propagation of shocks in such models (e.g., Boivin, Kiley, and Mishkin, 2010; Lindé, Smets, and Wouters,{{p}}2016). Return to text{{p}}6. For example, see Gertler and Kiyotaki (2010), Begenau (2015), Clerc et al (2015), and the review of models in Covas et al (2015). Return to text{{p}}7. For example, see Angelini et al (2015) and Begenau (2015). Return to text{{p}}8. For a discussion of the links between bank leverage banking crises, see Dagher et al (2016). Return to text{{p}}9. Challenges accounting for the interaction of distributional and aggregate risks has been a challenge in many areas (e.g., Malin, Kreuger, and Kubler, 2007).{{p}}For work on occasionally-binding constraints, see Guerrieri and Iacoviello (2014, 2015) and Maliar and Maliar (2015). Return to text{{p}}10. For example, see Akinci and Chahrour (2015). Return to text{{p}}11. For example, Guerrieri and Iacoviello (2015), Maliar and Maliar (2015), and Lindé, Smets, and Wouters (2016) examine solution and estimation with a{{p}}number of occasionally-binding constraints. Such considerations may be especially important in discussions of the potential for countercyclical capital buffers to{{p}}limit credit boom/bust cycles. In a model in which equity is costly and the countercyclical capital buffer is always binding, tightening the buffer during a boom and{{p}}easing the buffer during a bust lowers the amplitude of fluctuations in both directions; in a model in which the privately-desired capital ratio fall during booms and{{p}}rises during busts may involve changes over time in which constraint is binding, leading to more complex dynamics and potential challenges in gauging the{{p}}effects of a countercyclical capital buffer. Clerc et al (2015) consider a model in which the countercyclical capital buffer is assumed to bind at all times. Kiley and{{p}}Sim (2015) derive a time-varying market capital ratio that would interact with a regulatory capital constraint. Return to text{{p}}12. For example, see Akinci and Rumsey (2015). Return to text{{p}}13. One approach to assessing the implications of multiple Nash equilibria associated with runs would be to assume exogenous switching between the run and{{p}}no-run equilibria; such an assumption would illustrate the dynamics following a run, but cannot assess how policies affect the probability of a run. Return to text{{p}} FRB: FEDS Notes: Macroeconomic Modeling of Financial Frictions for Macroprudential ... Page 4 of 5{{p}} 5/26/2016{{p}}Last update: May 26, 2016{{p}}Home | Economic Research & Data{{p}}14. For example, Kiley (2016) illustrates how the signs of the response of the economy to technology shocks under a passive monetary policy across sticky-price{{p}}and flexible price versions of the same model, and how some models 'f price rigidity do not approach the flexible price limit as price rigidities are reduced. This{{p}}result is similar to the "fragility" of purely real models emphasize in Kocherlakota (2016). Return to text{{p}}Please cite this note as:{{p}}Kiley, Michael T. (2016). "Macroeconomic Modeling of Financial Frictions for Macroprudential Policymaking: A Review of Pressing{{p}}Challenges," FEDS Notes. Washington: Board of Governors of the Federal Reserve System, Publication_Date,{{p}}{{p}} Disclaimer: FEDS Notes are articles in which Board economists offer their own views and present analysis on a range of topics in{{p}}economics and finance. These articles are shorter and less technically oriented than FEDS Working Papers.{{p}}Accessibility Contact Us Disclaimer Website Policies FOIA PDF Reader{{p}} FRB: FEDS Notes: Macroeconomic Modeling of Financial Frictions for Macroprudential ... Page 5 of 5{{p}} 5/26/2016
    Date: 2016–05–26
  2. By: Francesco D’Acunto; Daniel Hoang; Michael Weber
    Abstract: Unconventional fiscal policy uses announcements of future increases in consumption taxes to generate inflation expectations and accelerate consumption expenditure. It is budget neutral and time consistent. We exploit a unique natural experiment for an empirical test of the effectiveness of unconventional fiscal policy. To comply with European Union law, the German government announced in November 2005 an unexpected 3-percentage-point increase in value-added tax (VAT), effective in 2007. The shock increased households' inflation expectations during 2006 and actual inflation in 2007. Germans' willingness to purchase durables increased by 34% after the shock, compared to before and to matched households in other European countries not exposed to the VAT shock. Income, wealth effects, or intratemporal substitution cannot explain these results.
    JEL: D12 D84 D91 E21 E31 E32 E52 E65
    Date: 2016–08
  3. By: Acikgoz, Omer
    Abstract: Aiyagari (1995) showed that long-run optimal fiscal policy features a positive tax rate on capital income in Bewley-type economies with heterogeneous agents and incomplete markets. However, determining the magnitude of the optimal capital income tax rate was considered to be prohibitively difficult due to the need to compute the optimal tax rates along the transition path. Contrary to this view, this paper shows that in this class of models, long-run optimal fiscal policy and the corresponding allocation can be studied independently of the initial conditions and the transition path. Numerical methods based on this finding are used on a Ramsey model calibrated to the U.S. economy. I find that the observed average capital income tax rate in the U.S. is too high, the average labor income tax rate and the debt-to-GDP ratio are too low, compared to the long-run optimal levels. The implications of these findings for existing literature on the optimal quantity of debt and constrained efficiency are also adressed.
    Keywords: Optimal Taxation, Ramsey Problem, Incomplete Markets, Heterogeneous Agents
    JEL: E2 E21 E25 E6 E62 H3 H6
    Date: 2015–06
  4. By: Michael T. Kiley
    Abstract: Print{{p}}November 23, 2015{{p}}Low Inflation in the United States: A Summary of Recent Research{{p}}Michael T. Kiley 1{{p}}Inflation in the United States has been running at low levels. Over the five years ending in December 2014, the percent change in the{{p}}Consumer Price Index (CPI), at 8.8 percent (or 1.7 percent at an annual rate), was the lowest rate of price increase seen in the United{{p}}States in half a century. To be sure, the past 50 years include the Great Inflation of the 1970s and the slow disinflation from those{{p}}elevated levels that occurred over the course of the 1980s and early 1990s. Nonetheless, inflation in recent years has fallen to levels{{p}}below the objective of the Federal Open Market Committee (FOMC), and the causes and potential consequences of low inflation have{{p}}been an area of intense research within the Federal Reserve System and elsewhere.2{{p}}In this note, several aspects of this research are reviewed and evaluated. The discussion first evaluates the degree to which low inflation{{p}}is a surprise: While the level of inflation is low by historical standards, the decline in inflation since the onset of the Great Recession has{{p}}been less than many popular models of the inflation process would have predicted and, as a result, research in recent years has{{p}}emphasized that inflation has been surprisingly high, not low. A number of factors have been offered as explanations for "surprisingly{{p}}high inflation", including an anchoring in inflation expectations, a small and/or reduced effect of unemployment on inflation (e.g., a{{p}}flattening of the Phillips curve), and an unusual divergence between unemployment and overall labor market slack that may have{{p}}pushed up inflation (e.g., "shadow slack" substantially less than that measured by unemployment). A brief review of each of these issues{{p}}is provided.{{p}}Before turning to the discussion, there are two factors that have likely played important roles in inflation developments in recent years{{p}}that receive only a short discussion herein. First, wage developments, and in particularly the interaction of low inflation and downward{{p}}nominal wage rigidity, have likely played a role; Daly and Hobijn (2014) review related evidence. In addition, the appreciation of the{{p}}dollar over the past year has been a factor in recent developments.{{p}}Low Inflation{{p}}Figure 1 presents the twelve-month percent changes in the CPI and the CPI excluding food and energy (the core CPI). Inflation in recent{{p}}years has been low by the standards of the past half century, and the recent decline in oil (and hence energy) prices is not an important{{p}}factor in explaining the low average pace of inflation in recent years, as the average pace of inflation since 2008 is essentially identical{{p}}for the overall and core CPIs.{{p}}Figure 1: Percent Change in CPI and Core CPI{{p}} Note: Percent change is the change over the previous twelve months. The data shown in the figure spans the period from January 1946 to September 2015.{{p}} Source: Department of Labor, Bureau of Labor Statistics{{p}} FRB: FEDS Notes: Low Inflation in the United States: A Summary of R...{{p}}1 of 6 11/23/2015 2:13 PM{{p}}Accessible version{{p}}A number of pieces of research, including well-known early investigations by Ball and Mazumder (2011) and Stock (2011), have noted{{p}}that the low rate of inflation following the Great Recession is not surprising given the rise in unemployment and a traditional Phillips{{p}}curve relationship – indeed, inflation has been surprisingly high. This surprise can be illustrated via graphical representation of the{{p}}simple accelerationist Phillips curve{{p}}π(t) = π(t - 1) - κ[U(t) - U*(t)] + e(t){{p}}where π(t) is inflation in period t, U(t) is the unemployment rate in period t and U*(t) is the natural rate of unemployment (so U(t)-U*(t) is{{p}}slack), κ>0 is the slope of the Phillips curve, and e(t) is the error term in the equation. Figure 2 presents a scatterplot of the change in{{p}}core CPI inflation against the unemployment rate, as suggested by this Phillips curve. Over the 1976-1995 period, a strong negative{{p}}relationship between the level of unemployment jumps out of the figure (and is captured in the fitted-regression line through the red{{p}}dots). In contrast, there is little relationship between the change in inflation and unemployment over the 1996-2014 period (the blue dots{{p}}and fitted line). Indeed, the decline in inflation since 2008 is surprisingly small using the pre-1996 relationship, as can be seen through a{{p}}comparison of the change in inflation when the unemployment rate is near 10 percent in the blue dots with the change that occurred in{{p}}the early 1980s recession (in the red dots for unemployment near 10 percent).{{p}}Figure 2: A Graphical Illustration of the Accelerationist Phillips Curve{{p}} Note: Core inflation is measured by the percent change in the annual average of the CPI from its level in the previous year.{{p}} Source: Department of Labor, Bureau of Labor Statistics and author's calculations.{{p}}Accessible version{{p}}Explaining the inflation surprise via changes in the Phillips curve{{p}}Many readers may not be especially surprised at some degree of instability in the type of reduced-form Phillips curve highlighted in{{p}}exhibit 2 – for example, instability in such relationships when the behavior of monetary policy changes significantly was a core critique of{{p}}such relationships over the past fifty years.3 But inspection of the accelerationist Phillips curve suggests two changes in its form that{{p}}could contribute to surprisingly high inflation.{{p}}First, lagged inflation in an accelerationist structure proxies for inflation expectations – perhaps because lagged inflation is a good{{p}}measure of the likely path of inflation in an environment in which the central bank's inflation target is uncertain and changing over time.{{p}}An alternative is that current inflation could be anchored by relatively stable inflation expectations, with such expectations replacing{{p}}lagged inflation in the relationship between inflation and unemployment. This effect is predicted by New-Keynesian Phillips curves with a{{p}}stable inflation target, as in Gali and Gertler (1999), Kiley (2007), or the current generation of dynamic-stochastic-general equilibrium{{p}}(DSGE) models used at central banks (which build on, for example, Smets and Wouters (2007)). The central prediction of this line of{{p}}thought – most prominently explored (either directly or indirectly) by Ball and Mazumder (2011), Stock (2011), and Coibion and{{p}}Gorodnichenko (2015) – is that inflation expectations in the 1976-1995 period presented in figure 2, during which an accelerationist{{p}}structure explains inflation movements well, should show a strong dependence on lagged inflation and, because of recent anchoring of{{p}}expectations, should show little dependence on lagged inflation in the 1996-2014 period. Figure 3 explores the relationship between{{p}}inflation expectations (inflation one-year ahead, as measured in the first quarter of the year) and the rate of CPI inflation in the previous{{p}}year (as measured by the percent change in the annual average of the CPI in the previous year).{{p}} FRB: FEDS Notes: Low Inflation in the United States: A Summary of R...{{p}}2 of 6 11/23/2015 2:13 PM{{p}}Figure 3: Inflation and Inflation Expectations{{p}} Note: Inflation in the previous year is measured by the percent change in the annual average of the CPI from the level in the previous year. Inflation{{p}}expectations (1-year ahead) are measured by the one-year expected inflation in GDP prices for the 1976-1995 period, as reported in the first-quarter Survey of{{p}}Professional Forecasters. For the 1996-2014 period, inflation expectations are measured by the one-year expected inflation in the CPI from the Survey of{{p}}Professional Forecasters.{{p}} Source: Department of Labor, Bureau of Labor Statistics and Federal Reserve Bank of Philadelphia.{{p}}Accessible version{{p}}The relationship between expected inflation and lagged inflation weakened substantially moving from the pre-1996 period to post-1995{{p}}period (the red to blue dots). As a result, an expectations-augmented Phillips curve would predict substantially less cumulative{{p}}disinflation in response to persistently elevated unemployment, as the anchoring of inflation expectations limits the degree of disinflation.{{p}}Even so, this anchoring is probably insufficient to explain the limited decline in inflation following the rise in unemployment in 2008 and{{p}} 2009: Returning to figure 2, both expected and actual (core) inflation were in the neighborhood of 2 percent during those years, and{{p}}hence the fact that inflation remained near this level when unemployment was near 10 percent (as can be seen by the small change in{{p}}inflation when unemployment was near 10 percent in the blue dots) points to factors other than anchored inflation expectations.{{p}}A second factor explored by Ball and Mazumder (2011), and emphasized by Del Negro, Giannoni, and Schorfheide (2015), is a "flat"{{p}}Phillips curve. Three (related) interpretations of "flatness" in the Phillips curve have been offered. Ball and Mazumder (2011) suggest{{p}}that "menu cost" models of nominal price and wage rigidity imply that such rigidities increase as the average rate of inflation falls,{{p}}implying that more of the adjustment in nominal aggregate demand falls on output and less on inflation when inflation is low; this is{{p}}exactly the finding emphasized in Kiley (2000), which analyzed support for this prediction across a large sample of countries. Research{{p}}exploring the effects of downward nominal-wage rigidity points to a reduced effect of labor-market weakness on inflation in a low-inflation{{p}}environment (Daly and Hobijn (2014)). In addition, Del Negro, Giannoni, and Schorfheide (2015) suggest that structural models of{{p}}inflation dynamics in the New-Keynesian tradition have long suggested "flatter" Phillips curves (e.g., Kiley (2007)) than apparent in the{{p}}reduced-form relationship depicted in figure 2 for the pre-1996 period; according to this view, the direct effect of slack on prices has long{{p}}been relatively small, and the apparent steep slope in the pre-1996 period owes to other factors (such as the challenges associated with{{p}}decisively shifting expectations to a view that monetary policy will act forcefully to stabilize inflation following a period in which such{{p}}actions were not taken). Finally, Kiley (2008) and Boivin, Kiley, and Mishkin (2010) present evidence that a more clear commitment to{{p}}price stability in recent decades, in the form of a monetary policy rule with a more sizable response to inflation, acts to substantially{{p}}stabilize inflation expectations and mitigate fluctuations in inflation, with less clear effects on economic activity; such a shift in monetary{{p}}policy behavior is consistent with an observed flattening in the Phillips curve.{{p}}While a flattening of the Phillips curve is consistent with a number of theoretical and empirical studies, the links between structural{{p}}features of the economy and the properties of the reduced-form relationship between inflation and unemployment is complex and{{p}}depends on many model features. Perhaps for this reason, analysis of the "missing disinflation" of recent years has not converged on{{p}}the role of a flattening in the Phillips curve. (For example, Christiano, Eichenbaum, and Trabandt (2015) use a DSGE model very similar{{p}}to that in Boivin, Kiley, and Mishkin (2010) and Del Negro, Giannoni, and Schorfheide (2015), but attribute the modest decline in inflation{{p}}relative to pre-1996 norms to a decline in technological progress, not a flat Phillips curve.){{p}}Explaining the inflation surprise via shadow slack{{p}}A number of researchers have linked the modest disinflation since 2008 to the notion that slack is not well-proxied over this period by the{{p}}unemployment rate. A fairly large number of researchers suggested that the very large (and persistent) increase in long-term{{p}} FRB: FEDS Notes: Low Inflation in the United States: A Summary of R...{{p}}3 of 6 11/23/2015 2:13 PM{{p}}unemployment, and the possibility that such unemployed persons are detached from the labor market to a sufficient degree that they do{{p}}not represent slack, may have been a factor that limited the disinflation over the period of interest.4{{p}}As figure 4 amply illustrates, the rise in long-term unemployment in the Great Recession was historically unusual, and short-term{{p}}unemployment had returned to a historically-typical level a couple of years ago, which would imply limited (to no) disinflationary impetus{{p}}from that time forward if short-term unemployment better captured slack. However, the econometric evidence using aggregate U.S.{{p}}labor-market measures was fairly inconclusive (e.g., Ball and Mazumder (2011)), and Kiley (2015), building on a model of the{{p}}econometric challenges in previous studies, found no evidence that short-term unemployment was a better measure of slack than total{{p}}unemployment using data on U.S. metropolitan regions.5 Moreover, long-term unemployment has now returned to a historically-typical{{p}}level and hence separate roles for short- and long-term unemployment could not be a factor in the most recent experience of low{{p}}inflation.{{p}}Figure 4: Total, Short-term, and Long-term Unemployment{{p}} Note: The long-term unemployment rate is defined as those unemployed for more than 26 weeks divided by the civilian noninstitutional population over age 16.{{p}}The short-term unemployment rate is the total unemployment rate minus the long-term unemployment rate. The shaded regions indicate short- or long-term{{p}}unemployment contributions to the total unemployment rate, which is the top edge of the shaded region. The data span the period from January 1948 to{{p}}October 2015.{{p}} Source: Department of Labor, Bureau of Labor Statistics and author’s calculations.{{p}}Accessible version{{p}}It is also important to keep in mind that, while historically unusual movements in long-term unemployment may have pointed to the{{p}}possibility that the total unemployment rate overstated slack in recent years, a number of other labor market indicators point in the{{p}}opposite direction. Most notable among these is the labor-force participation rate, which has been subdued (and declining) in the{{p}}post-2008 period: While Aaronson et al (2014) attribute the overwhelming majority of the decline in labor-force participation to{{p}}demographic factors, Erceg and Levin (2014) suggest a larger role for cyclical factors and the possibility that the low level of labor-force{{p}}participation indicates a substantial degree of "shadow slack". Smith (2014b) discusses a number of other labor-market indicators that{{p}}may have suggested slack greater-than-that suggested by the unemployment rate in recent years, including the number of employees{{p}}working part-time for economic reasons and the rate at which workers voluntarily leave their jobs.{{p}}Wrapping up{{p}}Inflation in the United States has been low. Low inflation is no surprise given the average condition of the economy in recent years –{{p}}indeed, relative to expectations based on the prior U.S. experience with high unemployment in the early 1980s, inflation has been{{p}}surprisingly high. Key factors include the anchoring of inflation expectations over this period and a "flattening" of the Phillips curve. But{{p}}the reasons behind such flattening are not clear.{{p}}The lowest pace of inflation over a sustained period in a half century has occurred at the same time as evidence that the equilibrium real{{p}}interest rate may be persistently lower than the norms of recent decades.6 This combination implies that nominal interest rates may{{p}}remain low by historical standards for some time, and raise the possibility that the U.S. economy could see nominal interest rates near{{p}}their effective lower bound (of approximately zero) to a substantially greater degree than expected or appreciated in earlier decades.{{p}}This possibility highlights an important role for research on monetary policy strategies that mitigate the effect of a lower bound on{{p}}nominal interest rates on price stability and economic activity.7{{p}}References{{p}} FRB: FEDS Notes: Low Inflation in the United States: A Summary of R...{{p}}4 of 6 11/23/2015 2:13 PM{{p}}Aaronson, Stephanie, Tomaz Cajner, Bruce Fallick, Felix Galbis-Reig, Christopher L. Smith, & William L. Wascher (2014). "Labor Force{{p}} Participation: Recent Developments and Future Prospects," Finance and Economics Discussion Series 2014-64. Board of Governors of{{p}}the Federal Reserve System (U.S.).{{p}}Ball, Laurence, & Mazumder, Sandeep, 2011. "Inflation Dynamics and the Great Recession." In: Brookings Papers on Economic Activity{{p}}(Spring), pp. 337–381.{{p}}Boivin, Jean & Kiley, Michael T. & Mishkin, Frederic S., 2010. "How Has the Monetary Transmission Mechanism Evolved Over Time?,"{{p}}Handbook of Monetary Economics, in: Benjamin M. Friedman & Michael Woodford (ed.), Handbook of Monetary Economics, edition 1,{{p}}volume 3, chapter 8, pages 369-422 Elsevier.{{p}}Christiano, Lawrence J. & Martin S. Eichenbaum & Mathias Trabandt, 2015. "Understanding the Great Recession," American Economic{{p}} Journal: Macroeconomics, American Economic Association, vol. 7(1), pages 110-67, January.{{p}}Chung, Hess, & Edward Herbst & Michael T. Kiley, 2015. "Effective Monetary Policy Strategies in New Keynesian Models: A{{p}}Reexamination," NBER Macroeconomics Annual, University of Chicago Press, vol. 29(1), pages 289 - 344.{{p}}Coibion, Olivier & Yuriy Gorodnichenko, 2015. "Is the Phillips Curve Alive and Well after All? Inflation Expectations and the Missing{{p}}Disinflation," American Economic Journal: Macroeconomics, American Economic Association, vol. 7(1), pages 197-232, January.{{p}}Daly, Mary C. & Bart Hobijn. 2014. "Downward Nominal Wage Rigidities Bend the Phillips Curve." FRB San Francisco Working Paper{{p}}2013-08.{{p}}Del Negro, Marco & Marc P. Giannoni & Frank Schorfheide, 2015. "Inflation in the Great Recession and New Keynesian Models,"{{p}}American Economic Journal: Macroeconomics, American Economic Association, vol. 7(1), pages 168-96, January.{{p}}Erceg, Christopher J. & Andrew T. Levin, 2014. "Labor Force Participation and Monetary Policy in the Wake of the Great Recession,"{{p}}Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(S2), pages 3-49, October.{{p}}Federal Open Market Committee (FOMC), 2014. "Longer-Run Goals and Policy Strategy," January 25.{{p}}Gali, Jordi & Gertler, Mark, 1999. "Inflation dynamics: A structural econometric analysis," Journal of Monetary Economics, Elsevier, vol.{{p}}44(2), pages 195-222, October.{{p}}Gordon, Robert J., 2013. "The Phillips Curve is Alive and Well: Inflation and the NAIRU During the Slow Recovery." NBER Working{{p}}Paper No. 19390, August.{{p}}Hamilton, James D., Ethan S. Harris, Jan Hatzius, & Kenneth D. West (2015) "The Equilibrium Real Funds Rate: Past, Present, and{{p}}Future," working paper (San Diego: University of California at San Diego, March).{{p}}Johanssen, Ben & Elmar Mertens (2015) "The Shadow Rate of Interest, Macroeconomic Trends, and Time-Varying Uncertainty." Mimeo.{{p}}Kiley, Michael T., 2000. "Endogenous Price Stickiness and Business Cycle Persistence," Journal of Money, Credit and Banking,{{p}}Blackwell Publishing, vol. 32(1), pages 28-53, February.{{p}}Kiley, Michael T., 2007. "A Quantitative Comparison of Sticky-Price and Sticky-Information Models of Price Setting," Journal of Money,{{p}}Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 101-125, 02.{{p}}Kiley, Michael T., 2008. "Inflation expectations, Uncertainty, the Phillips curve, and Monetary Policy - Comments," in Fuhrer, Jeffrey et al{{p}}(eds), Understanding Inflation and the Implications for Monetary Policy: A Phillips Curve Retrospective. The MIT Press, Cambridge, MA.{{p}}Kiley, Michael T., 2015. "An evaluation of the inflationary pressure associated with short- and long-term unemployment," Economics{{p}}Letters, Volume 137, December, Pages 5-9, ISSN 0165-1765, .{{p}}Kiley, Michael T., 2015. "What Can the Data Tell Us About the Equilibrium Real Interest Rate?," Finance and Economics Discussion{{p}}Series 2015-77, Board of Governors of the Federal Reserve System (U.S.).{{p}}Krueger, Alan, & Todd Cramer & David Cho, 2014. "Are the Long-term Unemployed on the Margins of the Labor Market." Papered{{p}}prepared for the Brookings Panel on Economic Activity, March 20–21.{{p}}Laubach, Thomas & John C. Williams (2015) "Measuring the Natural Rate of Interest Redux." Brookings Institution, October.{{p}}Lucas, Robert (1976). "Econometric Policy Evaluation: A Critique". In Brunner, K.; Meltzer, A. The Phillips Curve and Labor Markets.{{p}}Carnegie-Rochester Conference Series on Public Policy 1. New York: American Elsevier. pp. 19–46. ISBN 0-444-11007-0.{{p}}Smets, Frank & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American{{p}}Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.{{p}}Smith, Christopher L., 2014a. "The effect of labor slack on wages: Evidence from state-level relationships." Federal Reserve Board{{p}}FEDS Note, June 2.{{p}}Smith, Christopher L., 2014b. "Using cross-state variation to assess the potential for additional improvement in measures of labor market{{p}} FRB: FEDS Notes: Low Inflation in the United States: A Summary of R...{{p}}5 of 6 11/23/2015 2:13 PM{{p}}Accessibility Contact Us Disclaimer Website Policies FOIA PDF Reader{{p}}conditions." Federal Reserve Board FEDS Note, June 2.{{p}}Stock, James, 2011. "Comment on Inflation Dynamics and the Great Recession." In: Brookings Papers on Economic Activity (Spring),{{p}}pp. 387–402.{{p}}Watson, Mark W., 2014. "Inflation Persistence, the NAIRU, and the Great Recession." In: American Economic Review (Papers and{{p}}Proceedings), May.{{p}}1. Address: Board of Governors of the Federal Reserve System, Washington DC 20551; Email:; Phone: (1)202-452-2448. The author is Senior{{p}}Associate Director, Office of Financial Stability and Policy Research, and Senior Adviser, Division of Research and Statistics, at the Federal Reserve Board.{{p}}The views expressed herein are those of the author, and do not reflect those of the Federal Reserve or its staff. Return to text{{p}}2. The FOMC has defined its price objective as a rate of increase in the price index for personal consumption expenditures (PCE) of 2 percent per year{{p}}(FOMC, 2012). In this note, I use the CPI rather than the PCE price index because measures of expected inflation as measured by the CPI have a somewhat{{p}}longer history than is available for similar expectation measures for PCE prices. The qualitative and quantitative points emphasized herein do not hinge on the{{p}}choice of price index used to frame the discussion. Return to text{{p}}3. This argument is perhaps best represented by Lucas (1976). Return to text{{p}}4. Ball and Mazumder (2011) and Stock (2011) are early examples. Subsequent studies include Gordon (2013), Krueger (2014), and Watson (2014). Return to{{p}}text{{p}}5. Smith (2014a) presents a discussion of the links between short- and long-term unemployment and wages, along with references to related research. Return{{p}}to text{{p}}6. Recent studies include Hamilton et al (2015), Johanssen and Mertens (2015), Kiley (2015), and Laubach and Williams (2015). Return to text{{p}}7. For a discussion and analysis, see Chung, Kiley, and Herbst (2015). Return to text{{p}} Disclaimer: FEDS Notes are articles in which Board economists offer their own views and present analysis on a range of topics in{{p}}economics and finance. These articles are shorter and less technically oriented than FEDS Working Papers.{{p}}Last update: November 23, 2015{{p}}Home | Economic Research & Data{{p}} FRB: FEDS Notes: Low Inflation in the United States: A Summary of R...{{p}}6 of 6 11/23/2015 2:13 PM
    Date: 2015–11–23
  5. By: Ekaterina V. Peneva; Daeus Jorento; Emily Massaro
    Abstract: Print{{p}}Table 1: Granger causality between year-ahead inflation expectations of households (HH) and professional{{p}}forecasters (PF){{p}}Equation{{p}}number Sample Dependent{{p}}variable{{p}}Independent variables{{p}}R2{{p}}Constant{{p}}Sum of coefficients on{{p}}PF (8 lags) HH (8 lags){{p}}1 1981:Q3-2000:Q2 PF mean 0.59{{p}}(0.12){{p}}1.15{{p}}(0.00){{p}}-0.28{{p}}(0.18){{p}}0.87{{p}}2 1981:Q3-2000:Q2 HH mean 1.39{{p}}(0.01){{p}}0.61{{p}}(0.00){{p}}0.12{{p}}(0.67){{p}}0.70{{p}}3 1981:Q3-2000:Q2 PF median1 0.47{{p}}(0.14){{p}}1.05{{p}}(0.00){{p}}-0.22{{p}}(0.21){{p}}0.90{{p}}4 1981:Q3-2000:Q2 HH median1 0.65{{p}}(0.06){{p}}0.12{{p}}(0.20){{p}}0.66{{p}}(0.00){{p}}0.63{{p}}5 1995:Q1-2014:Q4 PF mean 0.37{{p}}(0.03){{p}}0.91{{p}}(0.00){{p}}-0.05{{p}}(0.22){{p}}0.87{{p}}6 1995:Q1-2014:Q4 HH mean 1.63{{p}}(0.00){{p}}-0.15{{p}}(0.30){{p}}0.66{{p}}(0.00){{p}}0.47{{p}} Note: P-values for the constants and the coefficients are in brackets.{{p}}October 7, 2015{{p}}Will Household Expectations Follow Professional Forecasters'?{{p}}Ekaterina Peneva, Daeus Jorento, Emily Massaro{{p}}In January 2012 the FOMC announced an explicit 2 percent objective for inflation as measured by the price index for Personal{{p}}Consumption Expenditures (PCE). A recent analysis of the effect of the FOMC's announcement by Detmeister et al. (2015) concluded{{p}}that the announcement "had some effect on professional forecasters' long-run inflation expectations, but not on household{{p}}expectations." However, even if the FOMC's announcement has not yet had an influence on household expectations, it still could affect{{p}}them indirectly in the future. Some existing academic research, for example Mankiw and Reis (2002), argues that agents do not update{{p}}their information sets frequently. Carroll (2003) suggests that when households do update their information sets, they update their{{p}}expectations using those of "more-informed" agents, such as professional forecasters. The learning from professional forecasters is not{{p}}necessarily because professional forecasters are better at predicting inflation (though they may be), but rather because households'{{p}}views derive mainly from news reports, which reflect the views of the professional forecasters.{{p}}Unfortunately, we find that some of the results from the existing research on how households form their inflation expectations are very{{p}} fragile: Specifically, the results--that households' expectations are updated toward the views of the professional forecasters--hold only for{{p}}particular expectations definitions and are generally much weaker if only the last 15-20 years are considered.{{p}}Following Carroll (2003), we start with examining Granger causality between households' and professional forecasters' expectations for{{p}}inflation over the next year.1 For households we use information on expected price changes from the Michigan Survey of Consumers{{p}}(MSC) and for professional forecasters we use the expectations of Consumer Price Index (CPI) inflation from the Survey of Professional{{p}}Forecasters (SPF). Using these data we can closely replicate Carroll (2003) results on Granger causality over the sample period 1981 to{{p}}2000.2 In particular over this time period, as Carroll found and as shown in equations 1 and 2 in Table 1, professional forecasters'{{p}}inflation expectations Granger-caused the households' expectations, but the households' expectations did not Granger-cause{{p}}professional forecasters' expectations. This implies a one-way information flow from more-informed professional forecasters to{{p}}less-informed households.{{p}}These results, however, are quite fragile. If, instead, the median expectation is used--Curtin (1996) suggests the median is preferable to{{p}}the mean when examining Michigan inflation expectations due to varying effect of extreme responses--then there is no evidence of{{p}}Granger causality--neither from households to professional forecasters, nor from professional forecasters to households over the 1981{{p}}to 2000 time period (equations 3 and 4).3 Similarly, if we use a more recent time period, from the beginning of 1995 to the end of 2014,{{p}}we cannot find the Granger causality from professional forecasters to households for either the mean measures (equations 5 and 6) or{{p}}for the median (not shown).{{p}}With the{{p}}Granger{{p}}causality{{p}}tests results{{p}}in hand,{{p}}Carroll{{p}}(2003){{p}}constructs a{{p}}baseline{{p}}model for{{p}}inflation{{p}} FRB: FEDS Notes: Will Household Expectations Follow Professional Fo...{{p}}1 of 4 10/8/2015 9:11 AM{{p}}1. We use the median of both HH and PF in this equation. Return to table{{p}}Table 2: Testing a model for the behavior of households' year-ahead inflation expectations{{p}}Equation{{p}}number Sample Dependent{{p}}variable{{p}}Independent variables{{p}}R2{{p}}Constant Current PF 1 lag of{{p}}Dependent variable{{p}}1 1981:Q3-2000:Q2 HH mean 0.99{{p}}(0.00){{p}}0.57{{p}}(0.00){{p}}0.26{{p}}(0.00){{p}}0.86{{p}}2 1981:Q3-2000:Q2 HH median1 0.78{{p}}(0.00){{p}}0.33{{p}}(0.00){{p}}0.37{{p}}(0.00){{p}}0.81{{p}}3 1995:Q1-2014:Q4 HH mean 0.99{{p}}(0.00){{p}}0.19{{p}}(0.31){{p}}0.61{{p}}(0.00){{p}}0.40{{p}}4 1995:Q1-2014:Q4 HH median1 0.93{{p}}(0.00){{p}}0.11{{p}}(0.41){{p}}0.60{{p}}(0.00){{p}}0.36{{p}} Note: P-values for the constants and the coefficients are in brackets. All standard errors are corrected for heteroskedasticity and serial correlation using{{p}}Newey-West procedure.{{p}}1. We use the median of both HH and PF in this equation. Return to table{{p}}Table 3: Granger Causality between long-term inflation expectations of households (HH) and professional{{p}}forecasters (PF){{p}}Equation{{p}}number Sample Dependent{{p}}variable{{p}}Independent variables{{p}}R2{{p}}Constant{{p}}Sum of coefficients on{{p}}PF (8 lags) HH (8 lags){{p}}1 1995:Q1-2014:Q4 PF mean 0.22{{p}}(0.05){{p}}1.01{{p}}(0.00){{p}}-0.08{{p}}(0.43){{p}}0.89{{p}}2 1995:Q1-2014:Q4 HH mean 0.54{{p}}(0.03){{p}}0.65{{p}}(0.05){{p}}0.35{{p}}(0.14){{p}}0.66{{p}}3 1995:Q1-2014:Q4 PF median1 0.30{{p}}(0.21){{p}}0.86{{p}}(0.00){{p}}0.01{{p}}(0.93){{p}}0.91{{p}}4 1995:Q1-2014:Q4 HH median1 0.83{{p}}(0.01){{p}}0.14{{p}}(0.10){{p}}0.59{{p}}(0.00){{p}}0.53{{p}}expectations, where households slowly update their macroeconomic views using those of the professional forecasters. In this simple{{p}}model, household inflation expectations are a weighted average of the current expectations of professional forecasters and the{{p}}expectations that household held in the previous period. We replicate this exercise, however, we also include a constant to account for{{p}}the historical mean difference between the expectations of household and professional forecasters, which could be due, among other{{p}}reasons, to somewhat different questions asked in the two surveys.4 Over the two decades ending in 2000 (equations 1 and 2 in Table{{p}}2), the information flow from professional forecasters to households was strong with the coefficient on professional forecasters' mean{{p}}expectations almost twice as high as the coefficient on households own lags, and these simple regressions explain over 80 percent of{{p}}the variation in household inflation expectations.5 However, in the most recent two decades these results fall apart: The weights the{{p}}regressions place on professional forecasters in determining household expectations fall by two-thirds and are no longer statistically{{p}}significant. The weight placed on households own lags doubles, and the regressions can now explain less than half of the variance in{{p}}household inflation expectations. The results hold both for the mean and the median of the household expectations--equations 3 and 4{{p}}in Table 2.6 The results for the most recent 20-year period are consistent with Pfajfar and Santoro (2013) finding based on micro data{{p}}that the households who update their expectations do not revise them toward the expectations of professional forecasters.{{p}}We now shift from examining household expectations of inflation over the next year to expectations of inflation over a longer-term{{p}}horizon, which, arguably, should be more influenced by monetary policy and the FOMC's announcement of a long-run inflation objective{{p}}than should expectations of inflation over the next year. We use the professional forecasters' expectations for CPI inflation over the next{{p}}10 years from the SPF and household expectations for inflation over the next 5 to 10 years from the MSC.7 In the last 20 years, as table{{p}}3 shows, there is no evidence that households' inflation expectations Granger-cause professional forecasters' expectations, but there is{{p}}evidence that professional forecasters' expectations Granger-cause households' inflation expectations if the means of those{{p}}expectations are used: The sum of the coefficients on lagged household expectations is small and insignificant in the regression{{p}}explaining professional forecasters' expectations (equation 1), but the sum of the coefficients on lagged professional forecasters'{{p}}expectations is large and significant in the regression explaining households' expectations (equation 2). However, if median, rather than{{p}}mean, expectations are used, the evidence for Granger causality from professional forecasters to households is much weaker--the{{p}}coefficient on lagged PF expectations is considerably smaller and barely statistically significant (equation 4).{{p}} FRB: FEDS Notes: Will Household Expectations Follow Professional Fo...{{p}}2 of 4 10/8/2015 9:11 AM{{p}} Note: P-values for the constants and the coefficients are in brackets. Durbin-Watson statistic did not indicate serial correlation in the residuals and the standard{{p}}errors are not corrected for serial correlation.{{p}}1. We use the median of both HH and PF in this equation. Return to table{{p}}Table 4: Testing a model for the behavior of households' long-term inflation expectations{{p}}Equation{{p}}number Sample Dependent{{p}}variable{{p}}Independent variables{{p}}R2{{p}}Constant Current PF 1 lag of{{p}}Dependent variable{{p}}1 1995:Q1-2014:Q4 HH mean 0.51{{p}}(0.02){{p}}0.43{{p}}(0.00){{p}}0.53{{p}}(0.00){{p}}0.68{{p}}2 1995:Q1-2014:Q4 HH median1 0.98{{p}}(0.00){{p}}0.16{{p}}(0.00){{p}}0.52{{p}}(0.00){{p}}0.55{{p}}3 2000:Q1-2014:Q4 HH mean 1.71{{p}}(0.00){{p}}0.05{{p}}(0.75){{p}}0.45{{p}}(0.00){{p}}0.17{{p}}4 2000:Q1-2014:Q4 HH median1 1.18{{p}}(0.00){{p}}0.05{{p}}(0.65){{p}}0.54{{p}}(0.00){{p}}0.28{{p}} Note: P-values for the constants and the coefficients are in brackets. All standard errors are corrected for heteroskedasticity and serial correlation using{{p}}Newey-West procedure.{{p}}1. We use the median of both HH and PF in this equation. Return to table{{p}}Figure 1: Survey Measures of Long-Run Expected Inflation, 1992:Q1-2014:Q4{{p}} Note: Median responses.{{p}}Accessible version{{p}}We also test Carroll's baseline learning model from table 2, replacing year-ahead inflation expectations with long-term inflation{{p}}expectations (see table 4). Using the past 20 years of data we can say that there is some evidence that households are paying attention{{p}}and using professional forecasters' expectations to update their own expectations of longer-term inflation:{{p}}In equations 1 and 2, the coefficients on professional forecasters' expectations are positive and significant, though considerably smaller{{p}}for the median than the mean. However, the size and the significance of the coefficients on professional forecasters' expectations is{{p}}entirely due to the first five years of the sample period: 1995-1999. If we limit the sample to the last 15 calendar years, the coefficients{{p}}on professional forecasters' expectations becomes very small and statistically insignificant.{{p}}The results found here, namely, that there is less evidence of households inflation expectations being updated with professional{{p}}forecasters' expectations over the recent past than in the 1980 to 2000 period, is perhaps not surprising, given that from the early 1980s{{p}}through the late 1990s actual inflation and both households' and professional forecasters' year-ahead inflation expectations fell{{p}}dramatically. It is quite possible that during this dynamic period households were paying close attention to what was being said about{{p}}inflation in the news. Inflation expectations of both professional forecasters and household, on the other hand, have been much less{{p}}variable over the past 15 years (Chart 1 shows the MSC long-run inflation expectations and the SPF expectations for long-run CPI{{p}}inflation, starting in the year they are both available). As Detmeister et al. (2015) noted, the announcement of the inflation objective{{p}}seemed to have an effect, albeit a small one, on the professional forecasters' expectations but not on the households' expectations. It is{{p}}quite possible, that in an environment of low and stable inflation, it is not worthwhile for households to update their inflation expectations{{p}}or to pay much attention to more-informed agents. If so--and if household expectations do play some role in wage and price setting--then{{p}}the SPF's convergence to a 2 percent rate of longer-term expected PCE inflation might not be particularly relevant for determining how{{p}}actual inflation will behave in coming years.{{p}}References{{p}}Carroll,{{p}}Christopher,{{p}}"Macroeconomic Expectations of Households and professional Forecasters," Quarterly Journal of Economics, 118 (2003), 269-298.{{p}} FRB: FEDS Notes: Will Household Expectations Follow Professional Fo...{{p}}3 of 4 10/8/2015 9:11 AM{{p}}Accessibility Contact Us Disclaimer Website Policies FOIA PDF Reader{{p}}Curtin, Richard, "Procedure to Estimate Price Expectations," Manuscript, University of Michigan Survey Research Center, (1996).{{p}}Detmeister, Alan, Daeus Jorento, Emily Massaro, and Ekaterina Peneva, "Did the Fed's Announcement of an Inflation Objective{{p}}Influence Expectations?" FEDS Notes, Board of Governors of the Federal Reserve System (U.S.), (2015).{{p}}Doepke, Joerg, Jonas Dovern, Ulrich Fritsche, and Jiri Slacalek, "The Dynamics of European Inflation Expectations." The B.E. Journal of{{p}}Macroeconomics, Vol. 8: Iss. 1 (Topics), (2008).{{p}}Mankiw, N. Gregory, and Ricardo Reis, "Sticky Information versus Sticky Prices: A Proposal to Replace the New Keynesian Phillips{{p}}Curve." Quarterly Journal of Economics, 117 (2002), 1295-1328.{{p}}Pfajfar, Damjan and Emiliano Santoro, "News on Inflation and the Epidemiology of Inflation Expectations." Journal of Money, Credit and{{p}}Banking, 45 (2013), 1045-1067.{{p}}1. Granger causality tests are tests of predictive content, in other words whether the independent variables have predictive content for the dependent variable.{{p}}Return to text{{p}}2. Our results--using current data--are very close but do not perfectly replicate Carroll's because historical data for the mean of household inflation{{p}}expectations was revised in 2006. Return to text{{p}}3. As Carroll (2003), for this analysis we use the estimated means and medians as reported by the MSC rather than the means and medians from the raw{{p}}micro data. Return to text{{p}}4. Carroll (2003) advocates against using a constant in a learning model as a non-zero constant implies that households' predictions are permanently biased{{p}}away from the professional forecasters' expectations or, alternatively, a non-zero constant implies that households are updating their expectations through{{p}}alternative channels. Given that the wedge between households' and professional forecasters' expectations have persisted in the years following Carroll's{{p}}study, despite the relatively low and stable inflation, we allow for a constant in all models we consider. Return to text{{p}}5. The results for the mean and for the 1981-2000 period are essentially the same as in Carroll (2003). Even if the median rather than the mean is used (see{{p}}equation 2), the coefficient on the PF expectations is relatively high and statistically significant, implying information flow from professional forecasters to{{p}}households. This is in contrast to the finding in Table 1 (equation 4) that median PF expectations do not Granger-cause the median HH expectations. The{{p}}difference arises because in the models presented in Table 2 current rather than past PF expectations are used to explain current HH expectations and{{p}}because lags 2 through 8 of the dependent variable are dropped: Either one of those changes can lead to a statistically significant coefficient on PF{{p}}expectations.{{p}}For Europe, Doepke et al. (2008) find that Carroll's sticky information model, in which households update their expectations using those of professional{{p}}forecasters, captures well the dynamics of households' inflation expectations in the four European countries they study albeit the frequency of updating being{{p}}lower than in the U.S. Return to text{{p}}6. As in Carroll (2003), we also allow for the possibility that households update their expectations using recent realized inflation. However, including the most{{p}}recent published annual headline CPI inflation in the equations in Table 2 does not alter the results in any meaningful way for either the 1981-2000 period or for{{p}}the 1995-2014 period. Recent inflation does not enter with a significant coefficient, except in equation 2, where it enters with a relatively small coefficient.{{p}}Return to text{{p}}7. The FOMC's inflation objective is in terms of inflation as measured by the PCE price index. However, since the SPF expectations for PCE inflation over the{{p}}next ten years starts only in 2007, we use the longer series – SPF expectations for CPI inflation, which is available on a quarterly basis since 1992. Return to{{p}}text{{p}} Disclaimer: FEDS Notes are articles in which Board economists offer their own views and present analysis on a range of topics in{{p}}economics and finance. These articles are shorter and less technically oriented than FEDS Working Papers.{{p}}Last update: October 7, 2015{{p}}Home | Economic Research & Data{{p}} FRB: FEDS Notes: Will Household Expectations Follow Professional Fo...{{p}}4 of 4 10/8/2015 9:11 AM
    Date: 2015–10–07
  6. By: Denis Gorea; Oleksiy Kryvtsov; Tamon Takamura
    Abstract: This note examines the merits of monetary policy adjustments in response to financial stability concerns, taking into account changes in the state of knowledge since the renewal of the inflation-targeting agreement in 2011. A key financial system vulnerability in Canada is elevated household indebtedness: as more and more households are nearing their debt-capacity limits, the likelihood and severity of a large negative correction in housing markets are also increasing. Adjusting the path of policy rates can be effective in reducing the buildup of household debt and the likelihood of a house price correction over the medium term. Such adjustments can also generate a fall in inflation and in output over the short term compared with the case without a policy-rate adjustment. Overall, the estimated benefits of a leaning adjustment tend to be smaller than its social losses, since its impact on the buildup of vulnerabilities is modest and the reduction in the incidence of house price corrections or financial crises is limited.
    Keywords: Financial stability, Monetary policy framework
    JEL: E0 E44 E52 E58 G18
    Date: 2016
  7. By: Stefania D'Amico; Thomas B. King; Min Wei
    Abstract: FEDS Notes Print{{p}}June 2, 2016{{p}}Macroeconomic Sources of Recent Interest Rate Fluctuations{{p}}Stefania D'Amico, Thomas B. King, and Min Wei 1{{p}}The authors use a new statistical method to attribute daily changes in U.S. Treasury yields and inflation compensation to changes in{{p}}investor beliefs about domestic and foreign growth, inflation, and monetary policy. They find that, while foreign developments have been{{p}}important drivers of U.S. yields and expected inflation over the last decade, the recent divergence between U.S. and European monetary{{p}}policy has had little effect. Instead, the behavior of asset prices seems consistent with positive "aggregate supply shocks." One candidate{{p}}for such shocks is the large decline in energy prices experienced since June 2014.{{p}}Between mid-2014 and late 2015, U.S. labor-market conditions continued to improve, and FOMC communications increasingly pointed to{{p}}a first interest rate hike in almost a decade, which occurred in December. We would have expected these developments to be associated{{p}}with a rise in longer-term Treasury yields, as often observed prior to past episodes of monetary policy tightening. Expectations for inflation{{p}}also usually increase prior to policy tightening, as the strengthening economy leads market participants to anticipate rising prices.{{p}}In stark contrast to this typical pattern, however, nominal U.S. Treasury yields declined slightly over this period, and a proxy for expected{{p}}inflation given by the spread between nominal yields and those on Treasury inflation-protected securities (TIPS), also known as inflation{{p}}compensation, decreased significantly. In this FEDS Note, we use new empirical methods based on comparisons of daily relative{{p}}movements in asset prices to explain this seemingly anomalous behavior.{{p}}One possible explanation for the puzzling movement in Treasury yields and inflation compensation that has received some attention is{{p}}that slowing global economic growth and disinflationary pressures abroad may have spilled over into U.S. markets over this period. For{{p}}example, over the last several years European developments have frequently figured prominently in the financial news, and, unlike the{{p}}Fed, the European Central Bank has aggressively eased policy in recent months. Our analysis is broadly consistent with these{{p}} observations: We find that global shocks were important drivers of U.S. yields and expected inflation over the last decade. However, those{{p}}shocks appeared to be less important during the run-up to the FOMC's recent rate increase.{{p}}Instead, we find that, between June 2014 and December 2015, market participants' expected U.S. growth trended higher, while expected{{p}}U.S. inflation trended lower. These two phenomena had offsetting effects on longer-term U.S. interest rates. This combination of stronger{{p}}growth and weaker inflation sounds very much like a classic "aggregate supply shock," i.e., an event that causes output and prices to{{p}}move in opposite directions. A natural candidate for such a shock is the large decrease in oil prices that began in the second quarter of{{p}}2014.{{p}}Recent trends in U.S. and European yields{{p}}The black lines in Figures 1 and 2 show daily movements in the 10-year nominal U.S. Treasury yield and 10-year TIPS inflation{{p}}compensation from June 2005 to December 2015.2 The large net declines in both yields and inflation compensation during the recent{{p}}financial crisis are consistent with the weaker growth, aggressive policy easing, and declining energy prices that buffeted the U.S.{{p}}economy in 2008 and 2009. Between early 2010 and mid-2014, yields drifted down further, amid continued easing by the Federal{{p}}Reserve, while inflation compensation fluctuated within a 100-basis-point range. Between mid-2014 and December 2015, yields and{{p}}inflation compensation have come down further, despite fairly steady data on economic growth and core inflation and the approaching first{{p}}rate hike in almost ten years. This seems particularly odd since, even before the actual rate increase, survey evidence shows that market{{p}}participants believed that the FOMC would act to increase short-term interest rates in the relatively near future.3 Typically, these beliefs{{p}}would be associated with increases in longer-term yields.{{p}}Figure 1: U.S. and Euro-Area 10-Year Sovereign Bond Yields{{p}} FRB: FEDS Notes: Macroeconomic Sources of Recent Interest Rate Fluctuations Page 1 of 5{{p}} 6/2/2016{{p}} Source: Thomson Reuters Tick History.{{p}}Accessible version{{p}}A partial resolution of these puzzles can be obtained by observing the behavior of the corresponding European data over the same{{p}}period. The red lines in the figures show the yield on 10-year German nominal government bond (the Bund) and 10-year Euro area{{p}}inflation compensation based on rates on inflation swaps contracts. Starting in the middle of 2007, U.S. and German bond yields, which{{p}}had previously displayed only a modest correlation, began moving together quite closely. This comovement increased further over the{{p}}period 2010 to 2012, when market participants--including those in U.S. Treasury markets--were focused on the Greek sovereign debt{{p}}crisis and the possible implications for global growth, as well as on the fiscal and financial stability of other EU member states. U.S. and{{p}}European inflation compensation were also broadly aligned during this time.{{p}}More recently, however, the behavior of U.S. and European longer-term yields has diverged somewhat. German yields declined by over{{p}}70 basis points between June 2014 and December 2015 while U.S. yields only fell about 16 basis points. Moreover, although the drop in{{p}}nominal yields was far bigger in Europe, the decline in European inflation compensation over this period was less than half of the drop in{{p}}the U.S. Consequently, it seems unlikely that European developments can fully explain why U.S. yields and inflation compensation{{p}}continued to be so low even in the face of improved economic activity and anticipated policy tightening.{{p}} Source: Thomson Reuters Tick History.{{p}}Accessible version{{p}}Empirical approach{{p}}To address these issues more systematically, we employ statistical time-series techniques to decompose daily changes in yields, inflation{{p}}compensation, and other financial variables into different types of "shocks." The shocks are distinguished by the directions in which they{{p}}Figure 2: U.S. and Euro-Area 10-Year Inflation Compensation{{p}} FRB: FEDS Notes: Macroeconomic Sources of Recent Interest Rate Fluctuations Page 2 of 5{{p}} 6/2/2016{{p}}move asset prices relative to one another. We assume that an unexpected increase in U.S. inflation expectations moves U.S. inflation{{p}}compensation and U.S. Treasury yields higher while also causing a depreciation of the dollar. Similarly, we assume that a surprise{{p}}increase in U.S. growth expectations will cause the U.S. stock market, U.S. Treasury yields, and the value of the dollar to shift up. Tighter-than-{{p}}expected U.S. monetary policy is assumed to increase Treasury yields and the value of the dollar but reduce the U.S. stock market{{p}}index and U.S. inflation compensation, and European policy shocks are identified in a similar fashion.4 These assumptions are intuitive{{p}}and can be justified with simple theoretical models. Global growth and inflation shocks are viewed as common factors driving key U.S.{{p}}and European variables in the same direction, but having a larger magnitude of impact on European variables than on U.S. variables. In{{p}}other words, we are favoring global shocks originating from Europe or from countries to which Europe has a larger exposure than the U.S.{{p}}does. Finally, we identify a "flight-to-safety" shock to control for the stylized fact that investors might seek safer assets in times of financial{{p}}stress. This shock is defined as one that moves U.S. nominal Treasury yields and the U.S. stock market down but U.S. stock market{{p}}volatility (as measured by the VIX index) up.{{p}}In summary, as shown in Table 1, each of the seven shocks under consideration is identified through a combination of restrictions that{{p}}have to be satisfied simultaneously. It is important to realize that, although we only observe the aggregate moves in asset prices each{{p}}day, we allow all seven of our shocks to occur on each day. Our statistical decomposition determines the magnitude and variation of each{{p}}of those shocks over our sample period.5 Importantly, it allows us to compute how much of the daily movement in each of the financial{{p}}variables can be attributed to each type of shock.{{p}}* "Global" shocks also restrict the magnitude of the impact on a European variable to be larger than that on the corresponding U.S. variable. Return to text.{{p}}Results{{p}}We analyze changes in U.S. nominal Treasury yields and inflation compensation in three sub periods that are chosen to loosely{{p}}correspond to the time-series patterns noted above. The first period starts at the end of July 2007 with the onset of the financial crisis and{{p}}ends on April 30, 2010, before the emerging of the European debt crisis. Intuitively, we would expect this time period to be dominated by{{p}}U.S. shocks associated with the financial crisis, although we also recognize that the financial crisis had international implications even at{{p}}this stage. The second sub period starts on April 30, 2010 and ends on May 30, 2014, and is meant to capture the intensification of the{{p}}European debt crisis. The last period, which runs from May 30, 2014 to December 16, 2015, is a period during which the U.S. and{{p}}European business and monetary-policy cycles were diverging. For example, during this period the Fed concluded its third round of QE{{p}}and signaled the approach of its policy rate increase, even as the ECB cut its deposit rate into negative territory and announced the{{p}}beginning of a large QE program. This period is also marked by significant declines in oil prices, which, though not explicitly included in{{p}}our model, will turn out to be an important part of our story.6{{p}}The columns in Table 2 report, for each sub period, the net changes in the 10-year U.S. Treasury yield and inflation compensation and the{{p}}portion of those changes that are attributed to each shock.7 Starting with the first period, out of a total decline of 109 basis points in the{{p}}10-year yield, 37 basis points are explained by U.S. shocks, with domestic growth accounting for the largest portion. This result is{{p}}consistent with the large negative shocks to the economy associated with the financial crisis. U.S. monetary policy accounts only for 11{{p}}basis points as, on balance, Fed communication and actions were slightly more accommodative than would have been expected given{{p}}the shocks to growth and inflation. Interestingly, foreign shocks also account for a similar share of the yield changes during this period (36{{p}}basis points). Finally, the flight-to-safety shock also plays a significant role, accounting for about 20 basis points of the total decline. As for{{p}}inflation compensation, in the first period, U.S. shocks account for a larger share, although the overall change is quite small.{{p}}For the second sub period, we find that the decline in the 10-year Treasury yield explained by U.S. shocks (-55 basis points) is a bit larger{{p}}than that explained by foreign shocks (-49 basis points), which now account for a bigger fraction of the total change relative to the{{p}}previous period. In the case of the U.S. shocks, a more-accommodative-than-expected monetary policy appears to have the largest effect.{{p}}This is not surprising, considering the significant balance sheet policies and forward guidance changes announced in this period. Negative{{p}}global inflation and growth shocks also contribute significantly to the downward movement in both U.S. yields and inflation compensation{{p}}over this period. This is consistent with the notion that, during this period, U.S. markets were driven in large part by disappointing news{{p}}from Europe.{{p}}Over the third sub period, we find that better-than-expected domestic growth and slightly tighter-than-expected domestic policy would{{p}}have pushed the U.S. yield about 30 basis points higher. However, most of this positive effect is offset by negative domestic inflation{{p}}shocks. Similarly, the largest contribution to the decline in inflation compensation also comes from negative U.S. inflation shocks. Relative{{p}}to the two earlier periods, global shocks have a smaller effect on U.S. yields but still account for nearly a third of the total downward{{p}}movement in inflation compensation. Furthermore, despite some notable monetary-policy developments in Europe during this period,{{p}}Table 1: Sign restrictions imposed to identify shocks{{p}}US growth US inflation US policy Global growth* Global inflation* EU policy Flight-to-safety{{p}}U.S. 10Y yield + + + + + -{{p}}S&P 500 index + - + -{{p}}U.S. 10Y breakeven + - +{{p}}VIX +{{p}}U.S. dollar vs. euro + - + -{{p}}German 10Y yield + + +{{p}}EuroStock600 index + -{{p}}EU 10Y infl. swap + -{{p}} FRB: FEDS Notes: Macroeconomic Sources of Recent Interest Rate Fluctuations Page 3 of 5{{p}} 6/2/2016{{p}}European policy shocks contribute little to the movement in U.S. yields and inflation compensation, suggesting that European policy{{p}}actions were largely in line with expectations. Finally, the flight-to-safety shock plays some role in explaining the movements of U.S. yields{{p}}and inflation compensation over this period.{{p}}The configuration of shocks in the last sub period could be consistent with oil price declines being an important driver of U.S. yields and{{p}}inflation compensation during this period. Large downward movements in oil prices, like those that occurred since the second half of 2014,{{p}}are potentially consistent with a variety of underlying forces, including a glut in oil production or weaker demand for this source of energy.{{p}}All else being equal, however, oil-price declines tend to reduce U.S. firms' costs, allowing them to increase their output without increasing{{p}}their prices. While our analysis does not directly examine the extent to which this has occurred, the pattern of positive movements in{{p}}expected growth and negative movements in expected inflation that we find suggests such a dynamic. The net effect of an oil-price{{p}}change on longer-term nominal yields is theoretically ambiguous and plausibly close to zero.8{{p}}Conclusions{{p}}In summary, we find that foreign developments explain a meaningful portion of the changes in U.S. nominal Treasury yields and inflation{{p}}compensation between mid-2007 and late 2015. However, after mid-2014, shocks that pushed expected U.S. growth and inflation in{{p}}opposite directions have been the most important driver of U.S. yields and inflation compensation movements, consistent with the large{{p}}decline in energy prices that occurred around this time. These developments left little net imprint on nominal yields and weighed down{{p}}inflation compensation, despite continued moderate U.S. growth and the anticipated beginning of the U.S. monetary-policy tightening{{p}}cycle.{{p}}1. D’Amico and King: Economic Research Department, Federal Reserve Bank of Chicago. Wei: Division of Monetary Affairs, Federal Reserve Board. We thank{{p}}Uri Carl, Eric Horton, and Vicki Eastman for excellent research assistance and Jim Clouse, Mike Joyce, and Anna Paulson for helpful comments. This article is a{{p}}reprint of D'Amico, King, and Wei, forthcoming, "Macroeconomic sources of recent interest rate fluctuations," Chicago Fed Letter. Return to text{{p}}2. All U.S. asset prices, including the dollar exchange rate, are recorded at noon New York time to be more in sync with European asset prices. Inflation{{p}}compensation is computed as the difference between nominal Treasury yields and TIPS yields of the same maturity. Technically, in addition to expectations of{{p}}inflation, it includes premiums for inflation risk and liquidity risk. We ignore those premiums for the purposes of this discussion, but it is important to keep in mind{{p}}that some of the movements in our measure may be driven by changes in the perceived risks of inflation, rather than with expectations per se. Return to text{{p}}3. For example, in January 2015, the Blue Chip Financial Forecasts survey indicated that 95% of respondents believed that the first rate hike would occur by the{{p}}September 2015 FOMC meeting. The Survey of Primary Dealers compiled by the Federal Reserve Bank of New York around the same time showed that the{{p}}median dealer attached 85% probability to the first rate increase occurring by the end of 2015. Return to text{{p}}4. Note that, unlike other approaches, this identification does not assume that the short-term interest rate is the sole indicator of the stance of monetary policy.{{p}}This is an important feature of our methodology since, following the financial crisis in 2008, the short rate remained essentially unchanged at zero and the Fed{{p}}instead began relying on unconventional monetary policies such as forward guidance and large-scale asset purchases. The effect of those policies should, in{{p}}principle, be captured by our approach. Return to text{{p}}5. Specifically, we use a structural vector autoregression (VAR) including the daily changes of the eight asset prices indicated above with structural shocks{{p}}partially identified using sign restrictions. The methodology builds on Zeno Enders, Gernot J. Muller, and Almuth Scholl, 2011, "How do fiscal and technology{{p}}shocks affect real exchange rates? New evidence for the United States," Journal of International Economics, Vol. 83, pp. 53-69; and Troy Matheson and Emil{{p}}Stavrev, 2014, "News and monetary shocks at a high frequency: A simple approach," IMF Working Paper No. 14/167. We restrict all intercepts in the VAR to be{{p}}zero, other than those in the stock-market equations, reflecting the notion that interest and exchange rates should not have deterministic drifts. Reported results{{p}}are based on the means of the posterior distributions from 10,000 draws. Return to text{{p}}6. The price of West Texas intermediate crude started to decline on June 7, 2014, and one year later was over 40% lower. Return to text{{p}}7. Note that there is some residual variation that is not explained by any of our shocks, so the shock contributions do not sum to the total change in each period,{{p}}and that the point estimates are subject to a considerable amount of sampling uncertainty. Return to text{{p}}8. For example, if the reduction in oil price reflects a price adjustment to overcapacity in oil production built up over the previous decade, as some market{{p}}commentaries have argued, it would provide additional stimulus to the economy, potentially offsetting the downward pressure on inflation and pushing up{{p}}nominal yields. Conversely, if the lower oil price resulted from investor pessimism about future global growth prospects and the associated demand for oil,{{p}}Table 2: Sub-period decomposition of changes in U.S. yields and inflation compensation{{p}}7/30/07-4/30/10 4/30/10-5/30/14 5/30/14-12/16/15{{p}}10y TY 10y IC 10y TY 10y IC 10y TY 10y IC{{p}}Total change -109 7 -122 -24 -16 -77{{p}}U.S. growth -24 4 -2 -2 22 -4{{p}}U.S. inflation -11 -4 -14 -5 -29 -30{{p}}U.S. policy -2 6 -39 -1 11 -7{{p}}Total -37 6 -55 -8 4 -40{{p}}Global growth -19 -4 -12 -6 -3 -10{{p}}Global inflation -9 -4 -26 -10 -9 -6{{p}}EU policy -7 6 -11 0 -2 -5{{p}}Total -36 -1 -49 -16 -14 -21{{p}}Flight to safety -20 0 -4 -0 -5 -9{{p}} FRB: FEDS Notes: Macroeconomic Sources of Recent Interest Rate Fluctuations Page 4 of 5{{p}} 6/2/2016{{p}}Last update: June 2, 2016{{p}}Home | Economic Research & Data{{p}}nominal yields would fall, reflecting both lower expected inflation and lower real yields. Our finding of a near-zero effect on nominal yields would be consistent{{p}}with supply forces being a more important factor behind the decline in oil price in the second half of 2014. Return to text{{p}}Please cite this note as:{{p}}D'Amico, Stefania, Thomas B. King, and Min Wei (2016). "Macroeconomic Sources of Recent Interest Rate Fluctuations," FEDS Notes.{{p}} Washington: Board of Governors of the Federal Reserve System, June 2, 2016,{{p}} Disclaimer: FEDS Notes are articles in which Board economists offer their own views and present analysis on a range of topics in{{p}}economics and finance. These articles are shorter and less technically oriented than FEDS Working Papers.{{p}}Accessibility Contact Us Disclaimer Website Policies FOIA PDF Reader{{p}} FRB: FEDS Notes: Macroeconomic Sources of Recent Interest Rate Fluctuations Page 5 of 5{{p}} 6/2/2016
    Date: 2016–06–02
  8. By: Elizabeth C. Klee; Zeynep Senyuz; Emre Yoldas
    Abstract: Print{{p}}December 21, 2015{{p}}Dynamics of Overnight Money Markets: What Has Changed at the Zero Lower Bound?*{{p}}Elizabeth Klee, Zeynep Senyuz, and Emre Yoldas{{p}}The primary instrument of monetary policy in the U.S. has been the interest rate at which depository institutions (DIs) lend balances to{{p}}each other overnight, the federal funds rate (FFR). Prior to the 2007-2009 financial crisis, daily open market operations had been{{p}}conducted to keep the equilibrium FFR near the target rate determined by the Federal Open Market Committee (FOMC). The response{{p}}of the Federal Reserve to the crisis significantly changed the landscape for implementation of monetary policy. The level of reserves in{{p}}the banking system reached unprecedented levels as a variety of new facilities and tools were used to provide liquidity to the financial{{p}}system.1{{p}}Against this backdrop, the Federal Reserve started paying interest on excess reserves (IOER) held by DIs at the end of 2008. Money{{p}}market rates have been near the effective zero lower bound (ZLB) since the target range of 0 to 0.25 percent for the FFR was{{p}}established in December 2008. In September 2014, the FOMC indicated that during the normalization of the stance of monetary policy, it{{p}}intends to move the FFR into a target range mainly by adjusting the IOER and use an overnight reverse repurchase (ON RRP) facility{{p}}and other supplementary tools as needed.2{{p}}In this note we provide a comparative analysis of overnight money market dynamics before the crisis and after the target FFR has been{{p}}lowered to the ZLB. In addition, we also zoom into the ZLB period and analyze the two sub-periods before and after the ON RRP facility{{p}}test operations have started in September 23, 2013. Overall, we find that the FFR has continued to be interconnected with the other{{p}}money market rates although the co-movement seems to have declined somewhat. The most notable changes in rate dynamics are{{p}}observed on calendar days including the financial reporting dates as well as the days of the reserve maintenance period of DIs.3 We{{p}}show that volatility in the repo market has substantially declined since the inception of the ON RRP facility.{{p}}Data and Methodology{{p}}The data set consists of five overnight money market rates, namely the FFR, the London Interbank Offered Rate (Libor), the Eurodollar{{p}}rate (ED), the primary dealer survey repo rate (RP), and the interest rate on AA-rated nonfinancial commercial paper (CP). We use the{{p}}daily series from January 2, 2001 to August 31, 2015, and exclude the part of the financial crisis episode until the beginning of the ZLB{{p}}period in December 2008.4 We use vector autoregressions and time-varying volatility models to analyze joint dynamics of the{{p}}aforementioned interest rates. In particular, the model for the pre-crisis period is a vector error correction (VEC) model that incorporates{{p}}the long-run equilibrium relationship of the rates as well as changes in the target FFR. For the ZLB period, given the stationary behavior{{p}}of rates, we estimate a vector autoregressive (VAR) specification in levels of the interest rates. Both models incorporate certain calendar{{p}}dates as well as the days of the maintenance period that may influence overnight funding rates. The ZLB model does not include target{{p}}FFR since it is constant for the entire period. In both cases, GARCH models with calendar effects are applied to model residuals to{{p}}capture time-varying volatility.{{p}}Results{{p}}Our estimates for the pre-crisis period are consistent with the monetary policy implementation framework in which FFR had been kept{{p}}near the FOMC target through open market operations, and market activity led to adjustments in other rates consistent with movements{{p}}in the FFR.5 In particular, we find that changes in the target FFR are highly significant in all rate equations, reflecting pass-through from{{p}}the policy rate to other overnight rates (Table 1, Panel A). Moreover, the parameters that quantify the adjustment of all interest rates{{p}}toward long-run equilibrium with the FFR are also highly significant, implying that it has been the other rates, not the FFR, that adjusted{{p}}in response to market fluctuations. We document significant autocorrelation dynamics in all rates, while cross lags are mostly{{p}}insignificant when we control for changes in target FFR and incorporate long-run equilibrium dynamics (Table 1, Panel B). At the ZLB,{{p}}although the FFR continued to provide an anchor for the unsecured overnight rates, the transmission to the repo rate is hampered as{{p}}implied by the insignificance of the lagged FFR in the RP equation (Table 2).{{p}}Table 1: The Pre-Crisis Model for Overnight Money Market Rates{{p}}Panel A. FFR Target Change and Error Correction Terms{{p}}FFR RP Libor CP{{p}}D(FFR) 0.454 0.406 0.337 0.416{{p}}(0.00) (0.00) (0.00) (0.00){{p}} FRB: FEDS Notes: Dynamics of Overnight Money Markets: What Has ...{{p}}1 of 5 12/28/2015 8:08 AM{{p}}FFR RP Libor CP{{p}}EC1 -0.032 0.303 -0.01 -0.001{{p}}(0.39) (0.00) (0.72) (0.98){{p}}EC2 -0.016 0.125 0.455 0.091{{p}}(0.91) (0.41) (0.00) (0.43){{p}}EC3 -0.005 0.021 -0.1 0.431{{p}}(0.97) (0.92) (0.21) (0.01){{p}}Panel B. Sum of Autoregressive Terms{{p}}FFR RP Libor CP{{p}}FFR -0.396 -0.057 -0.266 -0.12{{p}}(0.03) (0.82) (0.06) (0.58){{p}}RP -0.041 -0.692 0.171 0.071{{p}}(0.65) (0.00) (0.02) (0.42){{p}}Libor 0.728 0.258 -0.749 0.281{{p}}(0.04) (0.48) (0.00) (0.22){{p}}CP -0.767 0.143 0.506 -0.65{{p}}(0.06) (0.79) (0.02) (0.07){{p}}Estimates from a VEC model are reported. The daily sample runs from January 2, 2001 to July 31, 2007. P-values based on robust (HAC) standard errors are{{p}}reported in parenthesis. Lag length selected by Schwarz information criterion is 4. EC1, EC2, and EC3 denote the error correction terms implied by the{{p}}long-run equilibrium relationship of FFR with RP, Libor, and CP respectively.{{p}}Table 2: The ZLB Model for Overnight Money Market Rates{{p}}Sum of Autoregressive Terms{{p}}FFR RP Libor CP{{p}}FFR 0.911 0.107 0.223 0.153{{p}}(0.00) (0.21) (0.00) (0.02){{p}}RP 0.032 0.809 0.014 -0.011{{p}}(0.00) (0.00) (0.29) (0.47){{p}}ED -0.024 0.048 0.705 0{{p}}(0.53) (0.50) (0.00) (1.00){{p}}CP 0.036 0.054 0.069 0.881{{p}}(0.01) (0.14) (0.00) (0.00){{p}}Estimates from a VAR model are reported. The daily sample runs from December 17, 2008 to August 31, 2015. p-values based on robust (HAC) standard{{p}}errors are reported in parenthesis. Lag length selected by Schwarz criterion is 4.{{p}}The correlations of rates that are estimated after accounting for persistence and volatility dynamics also suggest continued{{p}}interconnectedness of FFR to other interest rates at the ZLB (Figure 1). The correlations of FFR with RP and CP declined somewhat at{{p}}the ZLB period, but remained sizable. However, financial reporting dates appear to have substantial effects on the comovement of FFR{{p}}and RP against the backdrop of abundant bank reserves and changing financial regulations. For example, the correlation of FFR and RP{{p}}on quarter-ends declined from 0.35 to effectively 0 at the ZLB.6{{p}}Figure 1: Correlations of FFR with the Other Rates: Pre-crisis vs. ZLB{{p}} FRB: FEDS Notes: Dynamics of Overnight Money Markets: What Has ...{{p}}2 of 5 12/28/2015 8:08 AM{{p}}Dots indicate point estimates and the surrounding bands are 95% confidence intervals. N denotes normal days that exclude month-end and quarter-end dates.{{p}}M and Q denote month-end and quarter-end respectively. Pre-crisis sample includes Libor, which is replaced with ED in the ZLB period.{{p}}Accessible version{{p}}Another notable change in the ZLB period relative to the pre-crisis era has been the disappearance of the day-of-maintenance-period{{p}}effects on the FFR, likely reflecting the abundance of bank reserves in the system. For example, the FFR used to be firmer on Mondays{{p}}possibly due to elevated payment flows while softer on Fridays since banks usually try to avoid an excess position over the weekend{{p}}during which reserves count for three days of requirement.7 In contrast, we find no statistically or economically significant day-of-maintenance-{{p}}period effects on the FFR in the ZLB period.{{p}}The dynamics surrounding financial reporting days changed notably at the ZLB amid abundant reserves in the banking system as well{{p}}as the introduction of new financial regulations. All rates were subject to modest upward pressure at month-ends prior to the crisis while{{p}}quarter-end effects had been somewhat more prominent (Figure 2, left). The most notable quarter-end effect had been the decline in the{{p}}repo rate, which likely reflected window-dressing activity that decreased demand for repo financing on such dates. The unsecured{{p}}financing rates had exhibited relatively smaller movements on quarter-ends, and in the opposite direction. In contrast, the quarter-end{{p}}effects turned negative for all unsecured rates at the ZLB amid the announcement and implementation of Basel III capital and liquidity{{p}}reforms. In particular, the Liquidity Coverage Ratio and leverage requirements seem to have reduced materially banks' demand for{{p}}short-term unsecured borrowing on reporting days.{{p}}Figure 2: Calendar Effects on Overnight Money Market Rates: Pre-crisis vs. ZLB{{p}}Dots indicate point estimates and the surrounding bands are 95% confidence intervals. M and Q denote month-end and quarter-end respectively. Effects are{{p}}normalized with respect to the standard deviations of model residuals.{{p}}Accessible version{{p}}Figure 3: RP Volatility and ON RRP{{p}} FRB: FEDS Notes: Dynamics of Overnight Money Markets: What Has ...{{p}}3 of 5 12/28/2015 8:08 AM{{p}}Estimated volatility of the RP from a GARCH model with month-end and quarter-end effects.{{p}}Accessible version{{p}}Contrary to the case of unsecured rates, the quarter-end effect has become insignificant for RP in the ZLB period. This estimate{{p}}captures the net effect as the ZLB period contains several quarter-end dates with material movements in RP in either direction. Such{{p}}changes are reflected in the statistically significant quarter-end effect on the volatility of the repo rate during the ZLB, which we estimate{{p}}to be around 2 basis points. As shown in Figure 3 the decline in RP volatility is strikingly evident both in the level and the volatility of the{{p}}series. Consistent with the intended effect of ON RRP to set a soft floor for repo rates, volatility in the repo market has substantially{{p}}dampened after the introduction of the program. We also find that calendar effects on RP volatility largely disappeared after that point.{{p}}Again, this likely reflects the soft floor placed on repo rates by the ON RRP, which lessen the potential for sharp falls in rates, as well as{{p}}the availability of the ON RRP as a viable investment, especially on financial reporting dates when other options may not be available.{{p}}Both results point out to a significant change in the structure of the money markets initiated by the test operations of the ON RRP facility.{{p}}References{{p}}Bech, M., E. Klee, and V. Stebunovs (2014): "Arbitrage, Liquidity and Exit: The Repo and Federal Funds Market before, during and{{p}}Emerging from the Financial Crisis', in Developments in Macro-Finance Yield Curve Modelling, ed. by J.S. Chadha, A.C. J. Durr, M.A. S.{{p}}Joyce, and L. Sarno, Cambridge University Press, 293-325.{{p}}Carpenter, S. and S. Demiralp (2006): "The Liquidity Effect in the Federal Funds Market," Journal of Money, Credit and Banking, 38,{{p}}901-920.{{p}}Hamilton, J. D., (1996): "The Daily Market for Federal Funds," Journal of Political Economy 104, 26-56.{{p}}Judson, R. and E. Klee (2010): "Whither the Liquidity Effect: The Impact of Federal Reserve Open Market Operations in Recent Years,"{{p}}Journal of Macroeconomics, 32, 713-731.{{p}}* We thank James Clouse, Jane Ihrig, and Josh Louria for helpful comments and Richard Sambasivam for research assistance. Return to text{{p}}1. The reserve balances of depository institutions currently stand close to $3 trillion compared to the pre-crisis level of about $25 billion. Return to text{{p}}2. See "Policy Normalization Principles and Plans" issued by the Federal Reserve on September 17, 2014{{p}}/monetary/20140917c.htm. Return to text{{p}}3. An institution is responsible for satisfying its reserve balance requirement by holding balances on average over a 14-day maintenance period in an account{{p}}at the Federal Reserve. For details of reserve maintenance, see Return to text{{p}}4. The daily effective FFR is calculated as the volume-weighted average of rates on trades arranged by major brokers and is available from the FRBNY. The{{p}}Treasury general collateral repo rate is a volume-weighted average on overnight repo transactions where the underlying collateral is U.S. Treasury security{{p}}and it is obtained from the primary dealer survey of the FRBNY. We use the ED data that the FRBNY started to publish in March 2010. Prior to that date, the{{p}}ED data are obtained from ICAP. For the pre-crisis period, Libor is substituted for the ED since the latter is not available. The CP rate is from the Federal{{p}}Reserve Board's CP release which is derived from data supplied by The Depository Trust and Clearing Corporation (DTCC). Return to text{{p}}5. See Bech et al. (2014) for a detailed analysis of repo and federal funds markets. Return to text{{p}}6. We also observe substantially lower correlations of the secured RP with other unsecured rates on financial reporting dates at the ZLB (not shown). Return to{{p}}text{{p}}7. See for example, Hamilton (1996), Carpenter and Demiralp (2006) and, Judson and Klee (2010). Return to text{{p}} Disclaimer: FEDS Notes are articles in which Board economists offer their own views and present analysis on a range of topics in{{p}}economics and finance. These articles are shorter and less technically oriented than FEDS Working Papers.{{p}} FRB: FEDS Notes: Dynamics of Overnight Money Markets: What Has ...{{p}}4 of 5 12/28/2015 8:08 AM{{p}}Accessibility Contact Us Disclaimer Website Policies FOIA PDF Reader{{p}}Last update: December 21, 2015{{p}}Home | Economic Research & Data{{p}} FRB: FEDS Notes: Dynamics of Overnight Money Markets: What Has ...{{p}}5 of 5 12/28/2015 8:08 AM
    Date: 2015–12–21
  9. By: Carlos Ballesteros
    Abstract: The paper develops a Dynamic Stochastic General Equilibrium (DSGE) model, which assesses the macroeconomic and labor market effects derived from simulating a positive shock to the stochastic component of the mining-energy sector productivity. Calibrating the model for the Colombian economy, this shock generates a whole increase in formal wages and a raise in tax revenues, expanding total consumption of the household members. These facts increase non-tradable goods prices relative to tradable goods prices, then real exchange rate decreases (appreciation) and occurs a displacement of productive resources from the tradable (manufacturing) sector to the non-tradable sector, followed by an increase in formal GDP and formal job gains. This situation makes the formal sector to absorb workers from the informal sector through the non-tradable formal subsector, which causes informal GDP to go down. As a consequence, in the net consumption falls for informal workers, which leads some members of the household not to offer their labor force in the informal sector but instead they prefer to keep unemployed. Therefore, the final result on the labor market is a decrease in the number of informal workers, of which a part are in the formal sector and the rest are unemployed.
    Keywords: Mining and energy boom, dutch disease, formal and informal sectors, unemployment, DSGE model
    JEL: E0 E1 E2 E3
    Date: 2016–08–01
  10. By: Cukierman, Alex
    Abstract: Persistent decreases in interest rates since the beginning of the twenty first century and the intensification of this trend with the onset of the global financial crisis nurtured the view that the natural rate is substantially lower than it used to be, and by some estimates, even persistently negative. Although investment activity depends mainly on risky rates existing estimates of the natural rate focus mainly on estimation of natural (mostly short term) riskless rates. Gilchrist and Zakrajsek (2012) find that, particularly during crisis times, risky and riskless rates tend to move in opposite directions and that the spread between risky and riskless rates is a good predictor of subsequent economic activity. Drawing on those findings the paper makes a case for conceptualizing and estimating a risky natural rate. This rate which better reflects the impact of the financial system on economic activity, is practically always bounded away from the zero lower bound. After documenting and reviewing the downward trend in world interest rates and the reasons underlying it the paper argues that recent post crisis estimates of the riskless natural rate are likely to be biased downward. Recent estimate of the (unobservable) natural rate are obtained by applying either the Kalman filter or Bayesian estimation to alternative standard versions of the New Keynesian (NK) model. The crisis substantially increased the tightening impact of credit rationing on the New Keynesian (NK) IS relation and the relative importance of the financial stability motive in the monetary policy rule. Since the standard NK model abstracts from credit rationing and from the financial stability motive existing estimates of the natural rate are likely to be biased downward, particularly so since the onset of the crisis.
    Keywords: downward bias in natural rate estimates; risky natural rate
    JEL: E3 E4 E5 G1
    Date: 2016–08
  11. By: Afanasyeva, Elena; Kuete, Meguy; Wieland, Volker; Yoo, Jinhyuk
    Abstract: The global financial crisis and the ensuing criticism of macroeconomics have inspired researchers to explore new modeling approaches. There are many new models that deliver improved estimates of the transmission of macroeconomic policies and aim to better integrate the financial sector in business cycle analysis. Policy making institutions need to compare available models of policy transmission and evaluate the impact and interaction of policy instruments in order to design effective policy strategies. This paper reviews the literature on model comparison and presents a new approach for comparative analysis. Its computational implementation enables individual researchers to conduct systematic model comparisons and policy evaluations easily and at low cost. This approach also contributes to improving reproducibility of computational research in macroeconomic modeling. Several applications serve to illustrate the usefulness of model comparison and the new tools in the area of monetary and fiscal policy. They include an analysis of the impact of parameter shifts on the effects of fiscal policy, a comparison of monetary policy transmission across model generations and a cross-country comparison of the impact of changes in central bank rates in the United States and the euro area. Furthermore, the chapter includes a large-scale comparison of the dynamics and policy implications of different macro-financial models. The models considered account for financial accelerator effects in investment financing, credit and house price booms and a role for bank capital. A final exercise illustrates how these models can be used to assess the benefits of leaning against credit growth in monetary policy.
    Keywords: Macro-Finance; macroeconomic models; model comparison; Monetary policy; policy robustness
    JEL: C3 C5 E1 E5 G1
    Date: 2016–08
  12. By: Garriga, Carlos (Federal Reserve Bank of St. Louis); Kydland, Finn E. (University of California–Santa Barbara and NBER); Sustek, Roman (Queen Mary University of London)
    Abstract: Standard models used for monetary policy analysis rely on sticky prices. Recently, the literature started to explore also nominal debt contracts. Focusing on mortgages, this paper compares the two channels of transmission within a common framework. The sticky price channel is dominant when shocks to the policy interest rate are temporary, the mortgage channel is important when the shocks are persistent. The first channel has significant aggregate effects but small redistributive effects. The opposite holds for the second channel. Using yield curve data decomposed into temporary and persistent components, the redistributive and aggregate consequences are found to be quantitatively comparable.
    Keywords: Mortgage contracts; sticky prices; monetary policy; yield curve; redistributive vs. aggregate effects.
    JEL: E32 E52 G21 R21
    Date: 2016–08–23
  13. By: Carlos Garriga (Federal Reserve Bank of St. Louis); Finn E. Kydland (NBER; University of California-Santa Barbara (UCSB)); Roman Sustek (CERGE-EI; Centre for Macroeconomics (CFM); School of Economics and Finance Queen Mary)
    Abstract: Standard models used for monetary policy analysis rely on sticky prices. Recently, the literature started to explore also nominal debt contracts. Focusing on mortgages, this paper compares the two channels of transmission within a common framework. The sticky price channel is dominant when shocks to the policy interest rate are temporary, the mortgage channel is important when the shocks are persistent. The first channel has significant aggregate effects but small redistributive effects. The opposite holds for the second channel. Using yield curve data decomposed into temporary and persistent components, the redistributive and aggregate consequences are found to be quantitatively comparable.
    Keywords: Mortgage contracts, Sticky prices, Monetary policy, Yield curve, Redistributive vs. aggregate effects
    JEL: E32 E52 G21 R21
    Date: 2016–08
  14. By: Carlos Arteta; M. Ayhan Kose; Marc Stocker; Temel Taskin
    Abstract: Against the background of continued growth disappointments, depressed inflation expectations, and declining real equilibrium interest rates, a number of central banks have implemented negative interest rate policies (NIRP) to provide additional monetary policy stimulus over the past few years. This paper studies the sources and implications of NIRP. We report four main results. First, monetary transmission channels under NIRP are conceptually analogous to those under conventional monetary policy but NIRP present complications that could limit policy effectiveness. Second, since the introduction of NIRP, many of the key financial variables have evolved broadly as implied by the standard transmission channels. Third, NIRP could pose risks to financial stability, particularly if policy rates are substantially below zero or if NIRP are employed for a protracted period of time. Potential adverse consequences include the erosion of profitability of banks and other financial intermediaries, and excessive risk taking. However, there has so far been no significant evidence that financial stability has been compromised because of NIRP. Fourth, spillover implications of NIRP for emerging market and developing economies are mostly similar to those of other unconventional monetary policy measures. In sum, NIRP have a place in a policy maker’s toolkit but, given their domestic and global implications, these policies need to be handled with care to secure their benefits while mitigating risks.
    Keywords: Unconventional monetary policy, quantitative easing; bank profitability, financial stability, negative yields, event study, emerging markets, developing countries
    JEL: E52 E58 E60
    Date: 2016–08
  15. By: Andrew Y. Chen; Eric Engstrom; Olesya V. Grishchenko
    Abstract: Print{{p}}April 4, 2016{{p}}Has the inflation risk premium fallen? Is it now negative?1{{p}}Andrew Chen, Eric Engstrom, Olesya Grishchenko{{p}}Inflation compensation is defined as the extra yield investors require to hold nominal assets that are exposed to inflation risk as opposed{{p}}to those that offer a safe inflation-adjusted return such as Treasury inflation protected securities (TIPS). Inflation compensation is widely{{p}}used by market commentators to gauge the expectations of investors regarding the outlook for inflation. Figure 1 depicts the daily time{{p}}series of one market-based measure of inflation compensation, defined as the difference between zero-coupon nominal and TIPS yields{{p}}of corresponding maturities.2 Since August 2014, measures of inflation compensation have trended down noticeably and, for many{{p}}months now, have been below 2 percent (the red line)--the rate of consumer price inflation targeted by the Federal Open Market{{p}}Committee (FOMC) over the longer-term.3{{p}}Figure 1: TIPS-based measures of inflation compensation.{{p}}Accessible Version{{p}} Source: Federal Reserve Board staff estimates{{p}}A straight read of these declines in inflation compensation might suggest that market participants expect inflation to fall significantly short{{p}}of the target rate of inflation, even at long horizons. However, other factors in addition to expected inflation likely affect inflation{{p}}compensation. In this note, we examine the theoretical determinants of one important component of inflation compensation, the inflation{{p}}risk premium, and argue that a secular decline in the inflation risk premium may be responsible for a substantial portion of the decline in{{p}}inflation compensation in recent years.{{p}}Measures of inflation compensation such as TIPS breakeven rates and inflation swap rates are related to market participants' expected{{p}}rate of inflation by the relationship:{{p}}Inflation compensation = expected inflation + inflation risk premium + other factors{{p}}Identifying the inflation risk premium is useful for measuring expected rate of inflation that is embedded in market prices, but it is also a{{p}}crucial quantity in its own right.4 For instance, if the premium is positive, then the government must pay an implicit positive premium for{{p}}issuing nominal Treasury securities relative to inflation-protected securities such as TIPS. However, if the inflation risk premium is{{p}}negative, then the relationship flips and issuing nominal bonds may be more cost effective for the Treasury.{{p}} FRB: FEDS Notes: Has the inflation risk premium fallen? Is it now negative?{{p}}1 of 4 4/4/2016 12:01 PM{{p}}Many market commentators appear to simply assume that the inflation risk premium is positive or ignore it altogether. According to these{{p}}commentaries, long-term nominal interest rates have generally exceeded short-term nominal interest rates, on average, to a greater{{p}}degree than is true for yields on securities that are protected from inflation risk such as TIPS. In the parlance of fixed-income analysts,{{p}}the nominal term structure has tended to be more steeply upward-sloping than the real term structure. This evidence is said to suggest{{p}}that assets like nominal bonds whose prices and returns suffer when inflation is unexpectedly high are viewed as risky by market{{p}}participants, who have then required a positive inflation premium, pushing up long-term nominal yields.{{p}}This logic is sound, but the available time series of data to support this claim is relatively short (in the United States, TIPS were first{{p}}issued in 1997). Further, as explored further below, there are good reasons to suspect that a structural change may have taken place{{p}}and that the slope of the nominal yield curve may be somewhat flatter in the future.{{p}}Conventional asset pricing theory suggests that the sign of risk premiums depends on the sign of the covariance of the returns of those{{p}}assets with the typical investors' consumption or wealth. For example, stocks require a high positive risk premium because equity prices{{p}}tend to fall during recessions, precisely when consumption also falls. Assets with payoffs tied to inflation are often modeled in this way{{p}}too.{{p}}Figure 2: Estimated correlations between 10-year forward consumption growth and long-run inflation.{{p}}Accessible Version{{p}}Figure 2 shows that the estimated correlation of long-run future inflation with long-run future consumption has not been stable over{{p}}time.5 The correlation was deeply negative in the 1980s, when periods of high inflation were associated with poor economic outcomes,{{p}}suggesting that the inflation risk premium was likely positive at that time. The negative correlation in the early sample is consistent with a{{p}}predominance of economic shocks that move inflation and real growth in opposite direction, such as oil price ("supply-side") shocks that{{p}}simultaneously raised inflation and lowered real economic activity.{{p}}However, Figure 2 shows that the correlation trended up over time and switched signs recently, implying that the risk premium may now{{p}}be negative. The change is consistent with an increasing role for "demand-side" shocks that instead push inflation and real economic{{p}}activity in the same direction. For instance, onset of the Great Recession saw both inflation and real activity plummeting simultaneously.{{p}}Using a-back-of-the-envelope calculation, we can roughly gauge the plausible magnitude of the negative risk premium. To do so, we{{p}}appeal to conventional asset pricing theory using a standard utility function with constant relative risk aversion and risk aversion{{p}}parameter, γ. Under these conditions, the inflation risk premium is{{p}}Inflation risk premium = -γ × covariance(inflation, consumption growth).{{p}}Using a risk aversion parameter of 20,6 the implied inflation risk premium at 10-year and 5-year 5 years ahead horizons at the end of the{{p}}sample is negative 17 basis points and negative 5 basis points, respectively, compared with positive average levels of about 100 basis{{p}}and 25 basis points for the two series in the 1980s.{{p}}However, there are important caveats to the above analysis. First, consumption-based asset pricing models have, at best, a mixed{{p}}record of fitting risk premiums across assets. Second, the particular statistical model of the correlation between consumption growth and{{p}}inflation depicted above may not coincide with investor perceptions.{{p}}To take a closer look at potential changes in the inflation risk premium over the past few years, we use higher-frequency data from asset{{p}}prices to estimate correlations between consumption growth and inflation depicted in Figure 2. We can use the capital asset pricing{{p}}model, according to which the risk premium associated with a position in inflation compensation (e.g. in inflation swaps) is:{{p}} FRB: FEDS Notes: Has the inflation risk premium fallen? Is it now negative?{{p}}2 of 4 4/4/2016 12:01 PM{{p}}Holding period inflation risk premium = market risk premium x beta(inflation compensation),{{p}}where the function "beta" is the usual concept that is proportional to the correlation between inflation compensation and equity returns.7{{p}}As Figure 38 depicts, the estimated betas of inflation compensation plunged into negative territory in 2009, similar to the results in Figure{{p}}2, and have remained negative ever since. Moreover, the betas have moved down sharply over the past few months.9 Also plotted is the{{p}}option-implied one month-ahead implied volatility on the S&P 500 index – the VIX – a measure plausibly related to the equity market risk{{p}}premium. The VIX has moved up somewhat, on net, over the past year as the betas have trended down, suggesting that the risk{{p}}premium associated with inflation compensation has become substantially more negative.{{p}}Figure 3. Beta of one-year ahead positions in inflation compensation with respect to the S&P500 index.{{p}}Accessible Version{{p}}To sum up, this note points out that standard consumption-based asset pricing models and the capital asset pricing model suggest that{{p}}the long run inflation risk premium has trended down over time, and is likely to be negative in the current macroeconomic environment.{{p}}Moreover, a nontrivial portion of the decline in far-forward inflation compensation over the past year may reflect a decline in the inflation{{p}}risk premium rather than a drop in investors' expected inflation rate.{{p}}A burgeoning academic literature has also investigated this issue, providing estimates of the inflation risk premium. For example,{{p}}Chernov and Mueller (2012) argue that the inflation risk premium estimates are model-dependent and can switch sign from positive to{{p}}negative in a model that accounts for survey-based inflation forecasts vs. the one that does exclude the survey forecasts from the{{p}}estimation. Grishchenko and Huang (2013) find that the inflation risk premium implied by the nominal yields and the TIPS-based{{p}}measure of inflation compensation was positive in the early 2000s but switched signs around the Great Recession. D'Amico, Kim, and{{p}}Wei (2014) also find that the inflation risk premium has been trending down. Finally, recent papers in the macro-finance literature have{{p}}made strides in incorporating the fundamental insights from consumption-based asset pricing into fully-fledged models of the term{{p}}structure of interest rates.10{{p}} References:{{p}}Bansal, R., Shaliastovich, I., 2013, "A long-run risks explanation of predictability puzzles in bond and currency markets," the Review of{{p}}Financial Studies, 26(1), pp. 1-33.{{p}}Chernov, M., Mueller, P., 2012, "The term structure of inflation expectations", Journal of Financial Economics, 106, pp. 367-394{{p}}Grishchenko, O., Huang, J., 2013, "The inflation risk premium: Evidence from the TIPS market", Journal of Fixed Income, 22(4), pp.5-30{{p}}Grishchenko, O., Song, Z., Zhou, H., 2015, "Term structure of interest rates with short-run and long-run risks," Finance and Economics{{p}}Discussion Series 2015-095. Washington: Board of Governors of the Federal Reserve System{{p}}Gurkaynak, R., Sack, B., Wright, J., 2007, "The U.S. Treasury yield curve: 1961 to present", Journal of Monetary Economics, 54, pp.{{p}}2291-2304{{p}}Gurkaynak, R., Sack, B., Wright, J., 2010, "The TIPS yield curve and inflation compensation", American Economic Journal:{{p}}Macroeconomics, 2(1), pp. 70-92{{p}}D'Amico, S., Kim, D., Wei, M., 2014, "Tips from TIPS: The informational content of Treasury Inflation-Protected Security Prices", Finance{{p}}and Economics Discussion Series 2014-24, Board of Governors of the Federal Reserve System (U.S.){{p}} FRB: FEDS Notes: Has the inflation risk premium fallen? Is it now negative?{{p}}3 of 4 4/4/2016 12:01 PM{{p}}Accessibility Contact Us Disclaimer Website Policies FOIA PDF Reader{{p}}Kocherlakota, N., 1996, "The equity premium: It's still a puzzle", Journal of Political Literature, 34(1), pp. 42-71{{p}}Koop, G., Korobilis, D., 2013, "Large time-varying parameter VARs", Journal of Econometrics, 177(2), pp. 185-198{{p}}Wachter, J., 2006, "A consumption-based model of the term structure of interest rates," Journal of Financial Economics, 79, pp. 365-399{{p}}1. Board of Governors of the Federal Reserve System. The views expressed in this note do not necessary reflect those of the Board of Governors, or its staff.{{p}}The note benefitted from helpful comments from Francisco Palomino, Anthony Diercks, and Michael Palumbo. Return to text{{p}}2. Zero-coupon nominal and TIPS yields are estimated using Svensson functional form of the yield curve. See Gurkaynak et al. (2007, 2010) for details. Return{{p}}to text{{p}}3. The FOMC targets Personal Consumer Expenditures (PCE) index while TIPS are indexed to Consumer Price Index (CPI). However, existing discrepancy{{p}}between the two indices is not likely to explain a recent downward trend in inflation compensation. Return to text{{p}}4. "Other factors" may include liquidity premiums and technical, usually transitory, trading effects. Return to text{{p}}5. These estimates were produced using a time-varying parameter vector autoregression (TVP-VAR) including the variables: the output gap (CBO estimate),{{p}}4-quarter core PCE inflation, real consumption growth, headline PCE inflation, and the Federal funds rate. Data are quarterly from 1954-2015. The TVP-VAR{{p}}methodology follows Koop (2013). Figure 2 shows the correlations between 10-year ahead consumption growth and 10-year ahead inflation (or 5-to-10 year{{p}}ahead inflation) implied by the TVP-VAR. Return to text{{p}}6. Risk aversion of 20 is a level typically found in the academic literature to be sufficient to fit the equity risk premium. See, for example, Kocherlakota (1996).{{p}}Return to text{{p}}7. The measured holding period risk premium is a slightly different concept from the inflation risk premium because it represents the excess return to holding a{{p}}position in inflation compensation for just one year. Return to text{{p}}8. The S&P 500 VIX ("Index") is a product of S&P Dow Jones Indices LLC and/or its affiliates and has been licensed for use by the Board. Copyright © 2016{{p}}S&P Dow Jones Indices LLC, a subsidiary of the McGraw Hill Financial Inc., and /or its affiliates. All rights reserved. Redistribution, reproduction and/or{{p}}photocopying in whole or in part are prohibited without written permission of S&P Dow Jones Indices LLC. For more information on any of S&P Dow Jones{{p}}Indices LLC's indices please visit S&P® is a registered trademark of Standard & Poor's Financial Services LLC and Dow Jones® is a{{p}}registered trademark of Dow Jones Trademark Holdings LLC. Neither S&P Dow Jones Indices LLC, Dow Jones Trademark Holdings LLC, their affiliates nor{{p}}their third party licensors make any representation or warranty, express or implied, as to the ability of any index to accurately represent the asset class or{{p}}market sector that it purports to represent and neither S&P Dow Jones Indices LLC, Dow Jones Trademark Holdings LLC, their affiliates nor their third party{{p}}licensors shall have any liability for any errors, omissions, or interruptions of any index or the data included therein. Return to text{{p}}9. We also use a TVP-VAR for this analysis. The endogenous variables include real and nominal Treasury yields at the 5- and 10-year maturities, the VIX{{p}}index, and returns on the S&P 500 index. Data are weekly from January 2010 - February 2016. Return to text{{p}}10. See Wachter (2006), Bansal and Shaliastovich (2013) and Grishchenko, Song and Zhou (2015). Return to text{{p}}Please cite this note as:{{p}}Chen, Andrew Y., Eric C. Engstrom, and Olesya V. Grishchenko (2016). "Has the inflation risk premium fallen? Is it now negative? ,"{{p}}FEDS Notes. Washington: Board of Governors of the Federal Reserve System, April 4, 2016,{{p}}/2380-7172.1720.{{p}} Disclaimer: FEDS Notes are articles in which Board economists offer their own views and present analysis on a range of topics in{{p}}economics and finance. These articles are shorter and less technically oriented than FEDS Working Papers.{{p}}Last update: April 4, 2016{{p}}Home | Economic Research & Data{{p}} FRB: FEDS Notes: Has the inflation risk premium fallen? Is it now negative?{{p}}4 of 4 4/4/2016 12:01 PM
    Date: 2016–04–04
  16. By: Taisuke Nakata
    Abstract: Figure 1: The Value of Commitment in a Stylized Model{{p}}August 27, 2015{{p}}Credibility of Optimal Forward Guidance at the Interest Rate Lower Bound{{p}}Taisuke Nakata{{p}}1. Introduction{{p}}Market participants and other analysts generally expect that the federal funds rate will rise from its effective lower bound (ELB) later this{{p}}year.1 However, the ELB could again become a binding constraint on monetary policy in the future.2 The ELB constraint prevents central{{p}}banks from further stimulating the economy through conventional means, making the economic conditions worse than they would{{p}}otherwise be. Accordingly, developing an effective strategy to address the adverse consequences of the lower bound constraint remains{{p}}an important task for economists and policymakers.{{p}}In this note, I first describe the effectiveness of a particular form of forward guidance policy--optimal commitment policy--in mitigating the{{p}}adverse consequences of the ELB. I then describe a key criticism against adopting this policy in reality--namely, that the policy is{{p}}potentially not credible. Finally, I discuss my research, Nakata (2014), that investigates how the central bank's concern for reputation can{{p}}overcome the credibility problem of the commitment policy.{{p}}2. Optimal Commitment Policy: A Strong Form of Forward Guidance{{p}}In many macroeconomic models, optimal commitment policy is very effective in mitigating the adverse consequences of the lower bound{{p}}constraint. Under this policy, the central bank commits to keeping its policy rate at the ELB for an extended period, with the explicit goal{{p}}of temporarily leading inflation and the output gap to overshoot their longer-run targets. In economies where households and price-setters{{p}}are forward looking, the temporary overheating of the economy tempers the declines in inflation and output during the period{{p}}when the lower bound is a constraint through improved expectations. This policy can be thought of as a particularly strong form of{{p}}forward guidance policy and has been analyzed by many economists.3 In particular, Michael Woodford of Columbia University made the{{p}}case for optimal commitment policy as a practical policy tool in a Jackson Hole presentation in 2012.4{{p}}Figure 2{{p}}illustrates{{p}}how this{{p}}commitment{{p}}policy works{{p}}in the{{p}}stylized{{p}} FRB: FEDS Notes: Credibility of Optimal Forward Guidance at the Intere...{{p}}1 of 6 8/27/2015 11:45 AM{{p}}Accessible version{{p}}macroeconomic model I use in Nakata (2014). In these simulations, there is a one-period crisis shock that hits the economy at period{{p}}one and disappears at period two. The blue and red lines show the dynamics of the federal funds rate, inflation, and the output gap{{p}}under optimal discretionary policy and under optimal commitment policy, respectively.5 Under the discretionary policy, the policy rate is{{p}}kept at the lower bound while the crisis shock lasts but returns to the steady state as soon as the crisis shock disappears. Inflation and{{p}}the output gap decline by 2 percentage points and 10 percentage points at period one, respectively. At period two, inflation is back to{{p}}target and the output gap is zero.{{p}}Under the commitment policy, the central bank keeps the policy rate at the ELB until period four. This policy generates an overshooting{{p}}of inflation and output at period two and beyond. Because price-setting is forward looking in this model, the higher expected inflation{{p}}after the crisis leads to higher actual inflation in the period of the crisis through the expectations term in the Phillips curve of the model.{{p}}Similarly, because households are forward looking, the higher expected output gap and lower expected real rate associated with higher{{p}}inflation mitigate the decline in the output gap in the crisis period through the expectations terms in the aggregate demand equation.{{p}}Inflation and the output gap thus decline only 0.3 and 6 percentage points respectively at period one.{{p}}3. A Case against the Commitment Policy: Lack of Credibility{{p}}While the commitment policy is effective in mitigating the adverse effects of the lower bound constraint, there is an important caveat:{{p}}Commitment may not be credible. That is, when the central bank announces this policy at the onset of the crisis, the private sector may{{p}}not believe that the central bank will stick to its commitment in the future. This tension arises because the central bank will have an{{p}}incentive to renege on the commitment. While the central bank wants to promise an extended period of low policy rates at the onset of{{p}}the crisis, once the crisis is over, the central bank is better off raising the policy rate and eliminating the overshooting of inflation and{{p}} FRB: FEDS Notes: Credibility of Optimal Forward Guidance at the Intere...{{p}}2 of 6 8/27/2015 11:45 AM{{p}}Figure 2. Costs and Benefits of Reneging on the Promise{{p}}output because such overshooting is undesirable ex post. In the academic literature, when the government ex post has an incentive to{{p}}renege on its promised policy action and thus the private sector does not believe in the government's promise, the policy is said to be{{p}}time-inconsistent. The credibility problem just described is a particular example of time-inconsistency.6{{p}}This time-inconsistency problem of optimal commitment policy is not a mere theoretical curiosity. This problem has been cited by{{p}}policymakers as a factor limiting the effectiveness of stimulating the economy through announcements about future policy actions. For{{p}}example, John Williams, president of the Federal Reserve Bank of San Francisco, stated:{{p}}The optimal forward guidance policy is not time-consistent. According to the theory, for this policy to have the desired effects, the central{{p}}bank must commit to two things: keeping the short-term policy rate lower than it otherwise would in the future, and allowing inflation to{{p}}rise higher than it otherwise would. However, when the time comes for the central bank to fulfill this commitment, it may not want to do{{p}}so. It might find it hard to resist the temptation to raise rates earlier than promised to avoid the rise in inflation. [Williams (2012)]{{p}}James Bullard, president of the Federal Reserve Bank of St. Louis, also acknowledges the difficulty of credibly committing to keeping the{{p}}policy rate low for long and permitting inflation to overshoot:{{p}}The "Woodford period" approach to forward guidance [i.e., optimal commitment policy] relies on a credible announcement made today{{p}}that future monetary policy will deviate from normal. The central bank does not actually behave differently today. One might argue that{{p}}such an announcement is unlikely to be believed. Why should future monetary policy deviate from normal once the economy is growing{{p}}and inflation is rising? But if the announcement is not credible, then the private sector will not react with more consumption and{{p}}investment today. That is, any effects would be minimal. [Bullard (2013)]{{p}}Similarly, Mark Carney, former governor of the Bank of Canada and current governor of the Bank of England, stated:{{p}}Today, to achieve a better path for the economy over time, a central bank may need to commit credibly to maintaining highly{{p}}accommodative policy even after the economy and, potentially, inflation picks up. Market participants may doubt the willingness of an{{p}}inflation-targeting central bank to respect this commitment if inflation goes temporarily above target. These doubts reduce the effective{{p}}stimulus of the commitment and delay the recovery. [Carney (2012)]{{p}}Some policymakers see this time-inconsistency problem as a key factor that makes central banks reluctant to adopt this commitment{{p}}policy in practice. According to Benoît Coeuré, board member at the European Central Bank,{{p}}The main challenge of such guidance [i.e., optimal commitment policy] is its inherent inconsistency over time and thus lack of credibility.{{p}}... This is a possible explanation why, in practice, central banks have refrained from using forward guidance in a way that implies a major{{p}}change in strategy. Therefore, central banks' forward guidance has rather aimed at providing greater clarity on the reaction function and{{p}}the assessment of future economic conditions. [Coeuré (2013)]{{p}}These quotes are only a few of many instances in which central bank officials have expressed their concern about the credibility problem{{p}}of the commitment policy, suggesting that concerns about credibility are an important consideration for many policymakers.7{{p}}4. Reputation as a Way to Overcome the Credibility Problem{{p}}In my research, I study whether the central bank's concern for reputation can make the commitment policy credible. My model combines{{p}}a theory of reputation from the game theory literature with a standard sticky-price macroeconomic model. In my model, if the central{{p}}bank reneges on its promise to keep the policy rate low for an extended period, it can eliminate overshooting of inflation and output in{{p}}the short run but it loses its reputation and the private sector will not believe similar promises in future recessions. Instead, the private{{p}}sector believes that the central bank will follow the discretionary policy, and the central bank loses its ability to conduct the commitment{{p}}policy. As just described, the discretionary policy would entail worse outcomes for inflation and the output gap than the commitment{{p}}policy. Thus, a concern for reputation creates an incentive for the central bank to fulfill its promises.8{{p}}However,{{p}}certain{{p}}criteria must{{p}}be met for{{p}}the{{p}}reputational{{p}}mechanism{{p}}to work. One{{p}}key{{p}}condition is{{p}}that the{{p}}crisis shock{{p}} FRB: FEDS Notes: Credibility of Optimal Forward Guidance at the Intere...{{p}}3 of 6 8/27/2015 11:45 AM{{p}}Accessible version{{p}}hits the{{p}}economy{{p}}sufficiently{{p}}frequently.{{p}}To illustrate{{p}}this point,{{p}}Figure 2{{p}}shows how{{p}}the crisis{{p}}probability{{p}}affects the{{p}}short-run{{p}}benefit and{{p}}the long-run{{p}}cost of{{p}}reneging on{{p}}the promise{{p}}of keeping{{p}}the policy{{p}}rate at the{{p}}lower bound{{p}}in the period{{p}}after the{{p}}crisis shock{{p}}disappears.{{p}}Here, the{{p}}short-run{{p}}benefit of{{p}}reneging on{{p}}the promise, shown by the red line, is how much the central bank gains by eliminating the overshooting of inflation and the output gap.{{p}}This short-run benefit does not depend on the crisis probability, hence this line is flat. The long-run cost, shown by the black line, is the{{p}}cost to the central bank of losing its reputation and thus its ability to conduct the commitment policy in the future. Because the benefits of{{p}}the commitment policy accrue during crises, this long-run cost increases with the crisis probability. Thus, the commitment policy is{{p}}credible only if the crisis probability is sufficiently high. In the example presented, the threshold crisis probability above which the{{p}}commitment policy is credible is very small, less than 0.1 percent per quarter. In the U.S., a naïve estimate of this crisis probability over{{p}}the past one hundred years would be about 0.5 percent per quarter, as there were two large shocks (the Great Depression and the{{p}}Great Recession) in which the Federal Reserve lowered the federal funds rate to its ELB in the 100 years since the creation of the{{p}}Federal Reserve (0.005=2/(4*100)).{{p}}I find that even a very small crisis probability is enough to make the commitment policy credible under many alternative assumptions{{p}}about the structure of the economy. This is true even when I modify the model to allow the chair at the central bank to change over time{{p}}so that loss of reputation is temporary. Thus, taken at face value, my results suggest that, if a central bank were to engage in this{{p}}commitment policy, reputational forces are likely to be strong enough to make it credible.{{p}}5. Conclusion{{p}}Optimal commitment policy has recently attracted a lot of attention among economists and policymakers as a potentially effective{{p}}approach to stimulating the economy when the short-term policy rate is constrained at the ELB. Despite its effectiveness in many{{p}}macroeconomic models, a number of central bank officials have expressed reservations about adopting such policies. One key reason{{p}}policymakers have reservations is this policy's time-inconsistency. My results suggest that a central bank's concern for its reputation can{{p}}make commitment policy time-consistent, potentially alleviating such concerns.{{p}}My framework permits the analysis of one key issue policymakers may want to consider for adopting the optimal commitment policy--{{p}}credibility. However, the validity of various assumptions responsible for making this optimal commitment policy effective would need to{{p}}be examined carefully if a central bank were to adopt this policy. For example, one key assumption of this theory is that households and{{p}}price-setters are forward looking and have rational expectations. Another is that the private sector correctly understands that the{{p}}overshooting of inflation from its longer-run target is temporary, and that long-run inflation expectations remain anchored at the central{{p}}bank's target.{{p}}Finally, while I have focused on the credibility of optimal commitment policy, my analysis is also useful in thinking about the credibility of{{p}}certain policy rules that share key aspects of the commitment policy, such as price-level targeting and nominal-income targeting rules.{{p}}Like the commitment policy, these rules also imply that the policy rate is kept at the lower bound for an extended period and that inflation{{p}}and the output gap overshoot their targets. My research suggests that, like the optimal commitment policy, a concern for reputation{{p}} FRB: FEDS Notes: Credibility of Optimal Forward Guidance at the Intere...{{p}}4 of 6 8/27/2015 11:45 AM{{p}}would make these rules credible should a central bank choose to adopt them.{{p}}References{{p}}Adam, K., and R. Billi (2006): "Optimal Monetary Policy Under Commitment with a Zero Bound on Nominal Interest Rates," Journal of{{p}}Money, Credit, and Banking, 38(7), 1877–1905.{{p}}Ball, L. M. (2013): "The Case for Four Percent Inflation," Central Bank Review, 13, 17–31.{{p}}Barro, R., and D. Gordon (1983): "Rules, Discretion, and Reputation in a Model of Monetary Policy," Journal of Monetary Economics, 12,{{p}}101–121.{{p}}Bean, C. (2013): "Global Aspects of Unconventional Monetary Policies," Remarks at the Federal Reserve Bank of Kansas City{{p}}Economic Policy Symposium, Jackson Hole, Wyoming.{{p}}Bullard, J. (2013): "Monetary Policy in a Low Policy Rate Environment," OMFIF Golden Series Lecture, London, United Kingdom.{{p}}Carney, M. (2012): "Guidance," Remarks at the CFA Society Toronto, Toronto, Ontario.{{p}}Coeuré, B. (2013): "The Usefulness of Forward Guidance," Remarks at the Money Marketeers Club of New York, New York City, New{{p}}York.{{p}}Dudley, W. (2013): "Remarks at the Central Bank Independence Conference: Progress and Challenges in Mexico," Remarks at the{{p}}Central Bank Independence Conference: Progress and Challenges in Mexico, Mexico City, Mexico.{{p}}Eggertsson, G., and M. Woodford (2003): "The Zero Bound on Interest Rates and Optimal Monetary Policy," Brookings Papers on{{p}}Economic Activity, 34(1), 139–235.{{p}}Jung, T., Y. Teranishi, and T. Watanabe (2005): "Optimal Monetary Policy at the Zero-Interest- Rate Bound," Journal of Money, Credit,{{p}}and Banking, 35(7), 813–35.{{p}}Kydland, F., and E. C. Prescott (1977): "Rules Rather than Discretion: The Inconsistency of Optimal Plans," Journal of Political Economy,{{p}}85(3), 473–493.{{p}}Lacker, J. (2013): "Monetary Policy in the United States: The Risks Associated With Unconventional Policies," Remarks at the{{p}}Swedbank Economic Outlook Seminar, Stockholm, Sweden.{{p}}Nakata, T. (2014): "Reputation and Liquidity Traps," Finance and Economics Discussion Series 2014-50, Board of Governors of the{{p}}Federal Reserve System (U.S.).{{p}}Nakov, A. (2008): "Optimal and Simple Monetary Policy Rules with Zero Floor on the Nominal Interest Rate," International Journal of{{p}}Central Banking, 4(2), 73–127.{{p}}Plosser, C. (2013): "Forward Guidance," Remarks at Stanford Institute for Economic Policy Re- searchs (SIEPR) Associates Meeting,{{p}}Stanford, California.{{p}}Rogoff, K. (1987): "Reputational Constraints on Monetary Policy," Carnegie-Rochester Conference Series on Public Policy, 26, 141–182.{{p}}Werning, I. (2012): "Managing a Liquidity Trap: Monetary and Fiscal Policy," Working Paper.{{p}}Williams, J. C. (2011): "Unconventional Monetary Policy: Lessons from the Past Three Years," FRBSF Economic Letter, 2011-31{{p}}(October 3).{{p}} ----(2012): "The Federal Reserve's Unconventional Policies," FRBSF Economic Letter, 2012-34 (November 13).{{p}}Woodford, M. (2012): "Methods of Policy Accommodation at the Interest-Rate Lower Bound," Presented at the 2012 Jackson Hole{{p}}Symposium, Federal Reserve Bank of Kansas City, Jackson Hole, Wyoming.{{p}}* I would like to thank Stephanie Aaronson, Eric Engen, David Lebow, Matthias Paustian, John Roberts, Gisela Rua, Robert Tetlow, and Bill Wascher for their{{p}} FRB: FEDS Notes: Credibility of Optimal Forward Guidance at the Intere...{{p}}5 of 6 8/27/2015 11:45 AM{{p}}Accessibility Contact Us Disclaimer Website Policies FOIA PDF Reader{{p}}thoughtful comments. Timothy Hills and Paul Yoo provided excellent research assistance. The views expressed in this note, and all errors and omissions,{{p}}should be regarded as those solely of the author, and do not necessarily reflect those of the Federal Reserve Board of Governors or the Federal Reserve{{p}}System.{{p}}** Division of Research and Statistics, Board of Governors of the Federal Reserve System, 20th Street and Constitution Avenue N.W., Washington, D.C.{{p}}20551; Email:{{p}}1. In the 2015 June Survey of Primary Dealers, respondents on average put about 80 percent probability to the event that the federal funds rate will rise from{{p}}the ELB by the end of 2015. Return to text{{p}}2. For example, Ball (2013) argues that "the lower bound on interest rates is likely to constrain monetary policy in a large fraction of recessions'' in the United{{p}}States. Return to text{{p}}3. See Adam & Billi (2006); Eggertsson & Woodford (2003); Jung et al. (2005); Nakov (2008); and Werning (2012). Return to text{{p}}4. See Woodford (2012). Return to text{{p}}5. Under the discretionary policy, the central bank optimizes its strategy every period based on the economic conditions that prevail at that time. Under the{{p}}commitment policy, the central bank optimally designs its strategy at period one and commits to implementing that strategy afterward. Return to text{{p}}6. Time-inconsistency of optimal commitment policy was first noticed by Kydland & Prescott (1977). The problem of time-inconsistency arises in many other{{p}}contexts when private-sector agents are forward looking. Return to text{{p}}7. See Bean (2013), Dudley (2013), Lacker (2013), Plosser (2013), and Williams (2011) for other examples. Return to text{{p}}8. The idea of making commitment policies credible by introducing reputational forces has a long history. Most famously, Barro & Gordon (1983) and Rogoff{{p}}(1987) used the same idea to ask whether a central bank can credibly commit to low inflation in the model where the central bank has short-run incentives to{{p}}create surprise inflation. Return to text{{p}} Disclaimer: FEDS Notes are articles in which Board economists offer their own views and present analysis on a range of topics in{{p}}economics and finance. These articles are shorter and less technically oriented than FEDS Working Papers.{{p}}Last update: August 27, 2015{{p}}Home | Economic Research & Data{{p}} FRB: FEDS Notes: Credibility of Optimal Forward Guidance at the Intere...{{p}}6 of 6 8/27/2015 11:45 AM
    Date: 2015–08–27
  17. By: Daniel A. Dias; Mark L. J. Wright
    Abstract: Figure 1: End 2015 debt repayment profile in percent of 2014 GDP for Greece, Portugal and Ireland{{p}}November 17, 2015{{p}}Debt Statistics a la Carte: Alternative Recipes for Measuring Government Indebtedness{{p}}Daniel A. Dias and Mark L. J. Wright1, 2{{p}}1. Introduction{{p}}According to Eurostat, the Greek government owed Euro 317 billion in debt at the end of 2014. This is equivalent to more than 177% of{{p}}GDP, 387% of tax revenue and amounts to almost Euro 30,000 per person. This seems like a very large sum. For comparison, of the{{p}}other highly indebted European countries that received financial assistance, Portuguese government debt amounted to 130% of GDP{{p}}while Irish government debt amounted to 110% of GDP.{{p}}As a result of these large debt numbers, there have been increasing calls for debt relief to be offered to Greece and, in some cases, also{{p}}to the other highly indebted countries of Europe. For example, the International Monetary Fund has recently announced that it considers{{p}}Greek debt unsustainable (IMF 2015a,b). Rogoff (2015a,b) also argues that Greek debt is unsustainable and has advocated a wider{{p}}European debt relief effort. On the other hand, wider debt relief found little support in a recent symposium of 30 economists{{p}}(International Economy, 2015), and numerous commentators have pointed to the fact that Greece's debt has been issued at low interest{{p}}rates and at long maturities to argue that Greece's debt is sustainable (for example, Gross 2015). Especially provocatively, some{{p}}commentators and investors have argued that, if measured under International Public Sector Accounting Standards (IPSAS), Greece's{{p}}debt could amount to as little as 70% of GDP (Soll, 2015, Giugliano, 2015).{{p}}Is Greece more or less indebted than either Portugal or Ireland? Which of the three countries is most burden by its debt? In our recent{{p}}research (Dias, Richmond and Wright 2014 and 2015), we have argued that in order to obtain answers to these questions it is necessary{{p}}to look closely at statistics on government indebtedness and the process by which these statistics have been constructed. We have also{{p}}proposed a number of measurement changes for sovereign indebtedness and shown how different statistics can give at times a radically{{p}}different impression as to indebtedness patterns throughout the developing world.{{p}}In this note, we apply our same measurement techniques to the debts of Greece, Ireland and Portugal and show that plausible{{p}}alternative measures of indebtedness suggest that Greece is anywhere from as much as 50% more indebted, to as little as half as{{p}}indebted as either Portugal or Ireland. We argue that most reasonable measures imply that Greece is far less indebted than is{{p}}commonly reported, and that indebtedness levels across these three economies are roughly similar.{{p}}2. The Measurement Problem{{p}}To illustrate the effect that measurement choices have on estimates of the relative indebtedness of countries, we created estimates of{{p}}the cash flows required to repay the debts of Greece, Ireland and Portugal as they are projected to be at the end of 2015. In light of the{{p}}scant available details, we do not adjust cash flows for the most recent Greek bailout announced in August. The data we use come from{{p}}multiple sources. Data on expected Greek debt payments is taken from the Wall Street Journal (WSJ), published in June 2015. The{{p}}article contains information about all future principal repayments Greece is expected to make from mid-2015 until end-2057. Data on{{p}}interest rates come from the WSJ, IMF and EFSF reports, and Zettelmeyer, Trebesch and Gulati (2013). In comparison to the aggregate{{p}}official values at the end of the first quarter of 2015, our data on principal repayments are about 10% smaller. Our data does not include{{p}}short term loans (repos), most of the debt to the European Investment Bank, and loans from the Greek central bank because we do not{{p}}have information about the distribution of future payments.{{p}}The data from Portugal and Ireland come almost entirely from their public debt management authorities (IGCP in Portugal and NTMA in{{p}}Ireland). The IGCP and NTMA publish data on expected principal repayments on its medium and long term debt, and also provide{{p}}information on interest rates which can be matched to the projected principal repayments. The data for Portugal is based on information{{p}}at end-January 2015, while for Ireland it is for the start of August 2015. In the cases of Portugal and Ireland, our data on principal{{p}}repayments are about 15% smaller than the official debt stock reported by the Eurostat. This difference is mostly due to non-tradeable{{p}}debt for which we were not able to find information beyond its face value.{{p}}With the data on principal repayments and corresponding interest rates, we estimate the annual interest payments that are expected at{{p}}every point in time from 2016 until the date of the last expected principal repayment. We take into account the existence of different{{p}}types of instruments (e.g., coupon bonds, amortizing bonds), as well as information on grace periods from official creditors such as the{{p}}EFSF.{{p}}The{{p}}resulting{{p}}projections{{p}}of cash{{p}} FRB: IFDP Note: Debt Statistics a la Carte: Alternative Recipes for Meas...{{p}}1 of 5 11/18/2015 3:58 PM{{p}} Source: Wall Street Journal (WSJ), Instituto de Gestão da Tesouraria de Credito Publico (IGCP), National Treasury Management Agency{{p}}(NTMA), EUROSTAT, and author's calculations.{{p}}Accessible version{{p}}flows,{{p}}scaled by{{p}}2014 GDP{{p}}for{{p}}comparability, are plotted in Figure 1. Blue bars represent principal repayments and red bars represent interest payments. Three facts{{p}}are immediately clear. First, interest payments make up a relatively small fraction of Greece's projected cash flows, reflecting the fact{{p}}that they have been able to borrow from official sources at subsidized interest rates. Second, both Portugal and Ireland face far larger{{p}}cash flow requirements, relative to the size of their economies, than Greece for the next ten years. Third, after this ten year period, the{{p}}required repayments on Greece's debt will far exceed those of Portugal and Ireland, measured as a fraction of their economies.{{p}}From these graphs it should be clear that whether or not we view Greece as more or less indebted than Portugal and Ireland depends{{p}}on how we weight cash flows in the near future (next ten years) versus cash flows in the far future (more than ten years). Different{{p}}statistics that weight these cash flows in different ways can be expected to give very different answers.{{p}}3. Alternative Recipes for Quantifying Government Debt{{p}}How do debt statistics currently weigh together these cash flows? For the most part, debt stocks are measured and reported at face{{p}}value [1]. Defined as the undiscounted sum of future principal repayments, face values simply add up the heights of the various blue{{p}}columns in each panel of Figure 1. Not surprisingly, Greece has a lot of debt when measured at face value; as shown in Table 1, the{{p}}face value of Greece's tradable debts totals 144.1% of GDP compared to 98.5% for Portugal and 92.5% for Ireland. That is, at face{{p}} FRB: IFDP Note: Debt Statistics a la Carte: Alternative Recipes for Meas...{{p}}2 of 5 11/18/2015 3:58 PM{{p}}Table 1. Debt stocks at the end of 2015 in billion euros and as percent of 2014 GDP based on face{{p}}Billion euros % GDP{{p}}Greece 258.1 144.1{{p}}Portugal 170.4 98.5{{p}}Ireland 169.7 91.5{{p}}Table 2. Debt stocks at the end of 2015 in billion euros and as percent of 2014 GDP based on zero coupon{{p}}equivalent values{{p}}Billion euros % GDP{{p}}Greece 318.8 178{{p}}Portugal 213.3 123.3{{p}}Ireland 230.7 124.4{{p}}Table 3. Debt stocks at the end of 2015 in billion euros and as percent of 2014 GDP based on 5 percent coupon{{p}}equivalent values{{p}}Billion euros % GDP{{p}}Greece 148.3 82.8{{p}}Portugal 150.9 87.2{{p}}Ireland 143.1 77.2{{p}}Table 4. Debt stocks at the end of 2015 in billion euros and as percent of 2014 GDP based on risk free present{{p}}values{{p}}Billion euros % GDP{{p}}Greece 258.6 144.4{{p}}Portugal 201.3 116.4{{p}}Ireland 206.4 111.3{{p}}Table 5. Debt stocks at the end of 2015 in billion euros and as percent of 2014 GDP based on market rates present{{p}}values{{p}}values, Greece is 57% relatively more indebted than Ireland, and 46% more indebted than Portugal.{{p}}As show in{{p}}Figure 1 by{{p}}the small{{p}}red{{p}}columns,{{p}}Greece has{{p}}been able to{{p}}borrow at{{p}}much lower{{p}}interest rates than either Ireland or Portugal. An alternative measure of indebtedness takes the undiscounted sum of all payments on{{p}}debt, principal plus interest (Dias, Richmond and Wright (2014) refer to it as the zero-coupon equivalent (ZCE) face value of the debt).{{p}}As shown in Table 2, the difference between the countries narrows somewhat when ZCE face values are used: Greece now has only{{p}}43% more debt than Ireland and 44% more than Portugal.{{p}}The{{p}}attractive{{p}}feature of{{p}}ZCE face{{p}}values is{{p}}that they{{p}}control for{{p}}differences{{p}}in the{{p}}contractual structure of a countries debts---differences in interest rates, maturities, and whether or not the debts were sold at par or at a{{p}}discount---and measure relative debt stocks under the assumption of a common portfolio of debt contracts. Of course, although they are{{p}}very useful for many purposes, there is no reason why we should focus exclusively on zero-coupon debts; we could as easily construct{{p}}5% coupon equivalent face values of debt. This turns out to be equivalent to simply measuring the present value of the cash flows{{p}}associated with a country's debts. Using a 5% interest rate assumption, Table 3 shows that Greece now has 7% more debt than Ireland{{p}}and 5% less debt than Portugal.{{p}}Is 5% the{{p}}right interest{{p}}rate to use?{{p}}It is certainly{{p}}a{{p}}conventional{{p}}choice (e.g.{{p}}Schumacher{{p}}and Weder,{{p}}2015, and IMF 2013). But in this era of very low interest rates, 5% may be a little high. One interesting calculation is to ask what the{{p}}value of these debts would be if they had been issued by a country with no risk of default, such as Germany. Using yield curves for{{p}}German government bonds, we can construct risk free present values, and present them in Table 4. Using this measure, Greece's debts{{p}}barely change as they are already borrowing at very low rates, while Portugal and Ireland's debts rise: Greece is now 30% more{{p}}indebted than Ireland and 24% more indebted than Portugal.{{p}}As one last{{p}}alternative,{{p}}we can{{p}}discount a{{p}}country's{{p}}entire debt{{p}}repayment{{p}}cash flows{{p}}by the{{p}}interest rates embodied in their currently traded debts to obtain an estimate of the market value of a country's debt. This assumes that{{p}}the likelihood of repayment of Greece's EFSF debt, for example, is the same as that for privately held bonds. Under these assumptions,{{p}}as shown in Table 5, Greece appears to have less than half as much debt as either Portugal or Ireland. These numbers are closer to the{{p}}estimates computed under the IPSAS standard, which records a debt at market value at the time of issue, and allows for the accretion of{{p}}this debt if the contracted interest rate on the debt is less than the yield to maturity of the debt. This approach has the counterintuitive{{p}}implication that the more likely a country is to default, the less indebted it will look.{{p}}In sum,{{p}}depending{{p}} FRB: IFDP Note: Debt Statistics a la Carte: Alternative Recipes for Meas...{{p}}3 of 5 11/18/2015 3:58 PM{{p}}Billion euros % GDP{{p}}Greece 75.5 42.1{{p}}Portugal 180.7 104.5{{p}}Ireland 194.3 104.8{{p}}Table 6. Debt stocks at the end of 2015 in billion euros and as percent of 2014 GDP based on social cost present{{p}}values{{p}}Billion euros % GDP{{p}}Greece 114 63.7{{p}}Portugal 125.3 72.4{{p}}Ireland 110.4 59.5{{p}}on the{{p}}choice of{{p}}statistic, we{{p}}can find that{{p}}Greece is{{p}}either half{{p}}again as{{p}}indebted as Ireland and Portugal, or less than half as indebted.{{p}}4. Is There a "Right" Debt Statistic?{{p}}In our previous work, we have argued that the right debt statistic to use is probably going to be quite sensitive to the precise purpose for{{p}}which the data will be used. In the case of forecasting default, for example, the right measure to use will probably also need to reflect the{{p}}particular circumstances of the country being considered. In some cases, it is also necessary to look at other statistics, such as net{{p}}interest expenses, as well as debt stock measures in order to obtain a complete picture of a country's debt portfolio. Forced to work with{{p}}one statistic, we have tended to emphasize ZCE face values as they are a theoretical upper bound on calculated face values and are{{p}}convenient for mapping to models which assume a zero coupon debt structure.{{p}}For some purposes, however, it may be possible to come up with a sufficient statistic that is useful in addressing a particular question{{p}}independently of the model assumed by the researcher. In Dias, Richmond and Wright (2015) we show that when a researcher is{{p}}interested in the welfare benefits of debt relief and debt forgiveness, it is sufficient to use the consumption capital asset pricing model{{p}}(CCAPM) to value a country's debt stock (or the change in that stock as a result of debt relief). In what follows, we estimate the CCAPM{{p}}under the assumption that real consumption growth rates in Euro's for each country follows a first order autoregressive process [2].{{p}}Table 6{{p}}shows that{{p}}the{{p}}equivalent{{p}}variation, in{{p}}terms of{{p}}current{{p}}period{{p}}consumption, of forgiving all of Greece's debts is less than that for Portugal and roughly the same as for Ireland. This reflects both the{{p}}time path of cash flows and the level of current consumption in each country. For Greece, current consumption is very low so that{{p}}forgiving far future cash flows, after consumption is expected to have recovered, is worth relatively little in terms of current consumption;{{p}}for Portugal, forgiveness is worth more because the required cash flows are higher in the near future when consumption is expected to{{p}}remain low.{{p}}Comparing Tables 5 and 6, we can see that the social cost of the debt (Table 6) is less than the market value of the debt (Table 5) for{{p}}both Portugal and Ireland. In normal times, this is the expected state of affairs: a country is a borrower precisely because they value{{p}}future debt repayments less than the market values them. If markets were perfect and borrowing unconstrained, Portugal and Ireland{{p}}would borrow until the two values were equated; the fact that they are not equated suggests that maturity extensions for both that{{p}}preserved market values would be Pareto-improving. [3]{{p}}For Greece, however, market values are less than social values, implying that debt forgiveness---a complete debt write-off---would{{p}}increase Greek welfare more than creditor welfare was reduced. This is under our assumption that official debts are valued at the rates{{p}}encoded in privately traded debts. If, alternatively, Greece's official debts were valued using risk free discounting (Table 4), the market{{p}}value would lie above the social cost and simple maturity extensions would be Pareto improving for all three countries.{{p}}5. Concluding Thoughts{{p}}Much of the debate about the debts of Greece and the rest of peripheral Europe has been informed by statistics that measure debt{{p}}stocks at face value and which show that Greece is significantly more indebted than Ireland and Portugal. We have shown that{{p}}alternative, and arguably preferable, measures of debt stocks paint a very different picture, with some suggesting that Greece is roughly{{p}}as indebted as either Ireland or Portugal. Measures based on the welfare cost of servicing debt, in particular, provide a case for debt{{p}}forgiveness for Greece and maturity extensions for Portugal and Ireland.{{p}}At the same time, GDP may not be the right measure of available resources to a country as it does not includes net factor income from{{p}}abroad. An alternative measure that takes this fact into account is GNI (Gross National Income). In the cases of Greece and Portugal,{{p}}this distinction is not very relevant, as the two measures are virtually identical for the two countries. In the case of Ireland this is not the{{p}}case. In 2014, GDP in Ireland was 185.4 billion euros, while GNI was 158.3 billion euros. This large difference means that if the{{p}}calculations shown above were presented in percent of GNI, all figures for Ireland would be 17% larger.{{p}} Endnote:{{p}}[1] For example, the World Bank (various) reports the face value of outstanding and disbursed debt. Until 1989, the U.S. statutory debt{{p}}limit was defined in terms of face values. Until 1997, Eurostat measured the face value of all debts; since 1997, it has departed from this{{p}}practice for zero-coupon and deep-discounted bonds (Eurostat 1997a,b).{{p}} FRB: IFDP Note: Debt Statistics a la Carte: Alternative Recipes for Meas...{{p}}4 of 5 11/18/2015 3:58 PM{{p}}Accessibility Contact Us Disclaimer Website Policies FOIA PDF Reader{{p}}[2] The implicit long run average per capita real consumption growth per year is 0.7% for Greece, 1.4% for Portugal and 1.8% for{{p}}Ireland.{{p}}[3] In the weeks since these calculations were completed, Greek yields have fallen and the market value of Greece's debt has increased,{{p}}which may reverse this conclusion and instead support further maturity extensions for Greece as well.{{p}} References:{{p}}Dias, Daniel A., Richmond, Christine, and Wright, Mark L. J. (2014), 'The Stock of External Sovereign Debt: Can We Take the Data at{{p}}`Face Value'?', Journal of International Economics, 94 (1), 1-17.{{p}}Dias, Daniel A., Richmond, Christine, and Wright, Mark L. J. (2015), 'In for a Penny, In for a Hundred Billion Pounds: Quantifying the{{p}}Welfare Effects of Debt Relief', unpublished paper.{{p}}EFSF,{{p}}Eurostat, 1997a. Deficit and debt: Eurostat rules on accounting issues. Eurostat Press Release 97 (10).{{p}}Eurostat, 1997b. Accounting rules: complementary decisions of Eurostat on deficit and debt. Eurostat Press Release 97 (24).{{p}}Giugliano, Ferdinando. 2015. Is Greek government debt really 177% of GDP? Financial Times. January 26.{{p}}Gross, Daniel. 2015. The Greek Austerity Myth. Project Syndicate. February 10.{{p}}IGCP,{{p}}International Economy. 2015. Does Europe Need Debt Relief: A Symposium of Views. Spring.{{p}}IMF,{{p}}IMF. 2013. Unification of Discount Rates Used in External Debt Analysis for Low-Income Countries. Washington, D.C.{{p}}IMF. 2014. Fiscal Monitor. October.{{p}}IMF, 2015a. Greece: Preliminary Draft Debt Sustainability Analysis. Country Report 15/165.{{p}}IMF. 2015b. Greece: An Update of IMF Staff's Preliminary Public Debt Sustainability Analysis. Country Report 15/186.{{p}}NTMA,{{p}}OECD. 2015. National Accounts of OECD Countries, Financial Balance Sheets 2014.{{p}}Rogoff, Kenneth. 2015a. What is Plan B for Greece? Project Syndicate, February 2.{{p}}Rogoff, Kenneth. 2015b. A New Deal for Debt Overhangs?, Project Syndicate, August 4.{{p}}Schumacher, Julian and Weder di Mauro, Beatrice. 2015. Debt sustainability puzzles: Implications for Greece. VoxEU. July 12.{{p}}Soll, Jacob. 2015. Greece's Accounting Problem. New York Times. January 20.{{p}}World Bank. various. Global Development Finance. World Bank Washington D.C.{{p}}WSJ -{{p}}Zettlemeyer, Jeromin, Trebesch, Christoph and Gulati, Mitu, 2013, "The Greek Debt Restructuring: An Autopsy", CESifo WP 4333,{{p}}July.{{p}}1. We thank Christine Richmond for all her help in the writing of this note. Return to text{{p}}2. This note reflects the views of the authors and does not necessarily represent those of the Board of Governors of the Federal Reserve System, the Federal{{p}}Reserve Bank of Chicago, the Federal Reserve System, or the National Bureau of Economic Research. Return to text{{p}} Disclaimer: IFDP Notes are articles in which Board economists offer their own views and present analysis on a range of topics in{{p}}economics and finance. These articles are shorter and less technically oriented than IFDP Working Papers.{{p}}Last update: November 17, 2015{{p}}Home | Economic Research & Data{{p}} FRB: IFDP Note: Debt Statistics a la Carte: Alternative Recipes for Meas...{{p}}5 of 5 11/18/2015 3:58 PM
    Date: 2015–11–17
  18. By: Alireza Sepahsalari (Univesrity College London (UCL); Centre for Macroeconomics (CFM))
    Abstract: This paper investigates the importance of credit market frictions on labour market outcomes. I build a tractable search and matching model of the labour market with firm dynamics and hetero-geneity in productivity and size. Firms produce output using labour, which they hire in a frictional market modelled by a directed search approach, and capital which they rent period-by-period. First, I show that the interaction of search and financial frictions slows down the reallocation of labour and capital from low productivity to high productivity firms and therefore prolongs the recession following a financial shock. Second, I find that the credit tightening reduces the net employment of large and productive firms more than small and unproductive firms, consistent with recent empirical findings.Third, I find that the introduction of financial frictions enhances the ability of the model to explain the fluctuation and persistence observed in output and labour market flows during the great recession. In fact, the model can account for 50% of the increase in unemployment during the 2008-2010 recession.
    Keywords: Labour market frictions, Collateral constraints, Financial shocks
    JEL: E24 E44
    Date: 2016–01
  19. By: Benjamin K. Johannsen; Elmar Mertens
    Abstract: Print{{p}}February 9, 2016{{p}}The Expected Real Interest Rate in the Long Run: Time Series Evidence with the Effective{{p}}Lower Bound{{p}}Benjamin K. Johannsen and Elmar Mertens1{{p}}Introduction{{p}}In response to the global financial crisis, the Federal Open Market Committee lowered the target for the federal funds rate to a range of{{p}}0 to 25 basis points in December 2008, and maintained that target range until the end of 2015. Over that same period, longer-term{{p}}interest rates in the United States were at historically low levels. When the Federal Open Market Committee began its policy{{p}}normalization process in December 2015, it stated that "the federal funds rate is likely to remain, for some time, below levels that are{{p}}expected to prevail in the longer run."{{p}}In this note, we present estimates of the expected long-run level of the real federal funds rate, which--together with long-run inflation{{p}}expectations--makes up the level of the nominal federal funds rate that is expected to prevail in the long run. In recent years,{{p}}researchers and commentators have pointed to a possible decline in the longer-run normal level of the real federal funds rate that could{{p}}have been caused by a number of economic factors--such as a decline the trend rate of output growth or an aging population.2 We use a{{p}}statistical framework to estimate the expected long-run normal level of the real federal funds rate. To distinguish cyclical variations in the{{p}}real interest rate from those with longer-run consequences we condition our estimates on a measure of economic slack, inflation, and{{p}}the levels of short- and longer-term interest rates. Since the nominal federal funds rate has been at (or very near) its effective lower{{p}}bound (ELB) in recent years, we embed the concept of so-called shadow rates (further explained below) in a time-series model that{{p}}captures the joint dynamics of interest rates, inflation and economic slack. While we find some supporting evidence for a decline in the{{p}}expected longer-run level of the federal funds rate, our estimates also show that these results are surrounded by large amounts of{{p}}uncertainty.{{p}}Methodology and Data{{p}}We use a dynamic time-series model to characterize the evolution of the following four observable variables: the nominal federal funds{{p}}rate, the nominal yield on Treasury bonds with a maturity of five years, headline PCE inflation, and a measure of the unemployment gap{{p}}based on the Congressional Budget Office's (CBO) estimates of the natural rate of unemployment in the long term.{{p}}A key element of our modeling approach is a decomposition of data into trends and cyclical components. In macroeconomics, there is a{{p}}long history of distinguishing between permanent and transitory effects of economic disturbances (see, for example, Blanchard and{{p}}Fischer, 1989). Specifically, in the spirit of the frequently-used framework of Beveridge and Nelson (1981), we identify the trend{{p}}components in each variable from the long-run forecasts generated by our model for each variable. Assuming that monetary policy can{{p}}affect real variables only temporarily, the trend in real interest rates identified by our model can also be considered a measure of the{{p}}longer-run neutral equilibrium real rate.3{{p}}The following trend components are specified in our model: a common trend in nominal interest rates and an inflation trend; the{{p}}unemployment gap is expected to converge to zero in the long run.4 The common trend assumption for nominal interest rates implies{{p}}that fluctuations in interest rate spreads will eventually peter out without changes in long-term spreads. As a result, our model's long-run{{p}}forecasts for the federal funds rate and the nominal Treasury bond are restricted to move in lockstep.5 Furthermore, appealing to a{{p}}long-run version of the Fisher equation, the trend in the nominal federal funds rate is modeled as the sum of the inflation trend and the{{p}}real-rate trend, which we use to measure the longer-run normal level of the real federal funds rate. As a result, the expected long-run{{p}}level of the nominal federal funds rate can change over time either because of variations in trend inflation or because of changes in the{{p}}longer-term expected real rate.{{p}}The trend component of inflation allows our model to capture the persistent rise of the average level of inflation in the 1970s and its{{p}}subsequent decline. The variability and persistence of inflation has varied considerably over our sample (see for example Cogley,{{p}}Primiceri and Sargent, 2010) and we build on the work by Stock and Watson (2007, 2010, 2015), Garnier, Mertens and Nelson (2015),{{p}}Shephard (2016) and Mertens (forthcoming) by assuming that the volatility of the trend component of inflation--as well as the variability{{p}}of cyclical shocks to inflation as explained in more detail below--varies over time.6{{p}}We model the cyclical components of inflation, the federal funds rate, the long-term interest rate, and the unemployment gap as a{{p}}vector-autoregressive process. In order to capture the notable variations in the size of the business cycle in post-war U.S. data (see{{p}}Bernanke (2004) for a detailed discussion), the gap components of our model are assumed to have time-varying volatility; as in, for{{p}}example, Justiniano and Primiceri (2008). Modeling the cyclical components in a joint vector autoregression captures the co-movements{{p}}and interactions between current and past values of each of the variables in a parsimonious and fairly agnostic way. Among others, this{{p}} FRB: FEDS Notes: The Expected Real Interest Rate in the Long Run: Ti...{{p}}1 of 6 2/10/2016 8:15 AM{{p}}modeling strategy allows the federal funds rate to respond to movements in economic slack--as measured by the unemployment rate{{p}}gap--and cyclical variations of inflation. It also allows the unemployment rate gap to move in conjunction with changes in financial{{p}}conditions that are captured by the cyclical component of the yield on longer-term Treasury bonds or for cyclical fluctuations in inflation{{p}}to depend on changes in the unemployment rate gap.{{p}}Our model is estimated using quarterly data ranging from 1960:Q1 to 2015:Q4; all data is publicly available from the FRED database{{p}}maintained by the Federal Reserve Bank of St. Louis.7 Inflation is measured by the quarterly rate of change in the PCE headline deflator{{p}}(expressed as an annualized percentage rate).8 Readings for the federal funds rate and the 5-year nominal bond yields are constructed{{p}}as quarterly averages of the effective federal funds rate and the Treasury's 5-year constant maturity rate, respectively.9 The{{p}}unemployment gap is computed as the difference between the quarterly average rate of unemployment and the CBO's measure of the{{p}}natural long-term rate of unemployment for a given quarter.10 All computations are based on the vintage of FRED data available that has{{p}}been available at the end of January 2016.{{p}}Modeling Interest Rates at the Effective Lower Bound{{p}}A challenge for our statistical model is that, from late 2008 to late 2015, the federal funds rate was at (or very near) the ELB. This lower{{p}}bound on nominal interest rates potentially alters the co-movement between the federal funds rate and the other data in our sample.{{p}}Additionally, the non-linearity of the ELB is often ignored by traditional time-series methods. To overcome this technical challenge, we{{p}}define the "shadow" federal funds rate, which is a notional rate that is constructed to be less than the zero during the period that the{{p}}target range for the federal funds rate was between 0 and 25 basis points. During all other times, the shadow rate is assumed to equal to{{p}}the federal funds rate. In this way, the observed nominal federal funds rate can be thought of as a censored version of the shadow rate.{{p}}At the ELB we do not observe the shadow rate, but estimates of the shadow rate can be generated based on the historical persistence{{p}}and co-movements between the federal funds rate and the other series in our model.{{p}}Several other studies have used the term "shadow rate" to refer to a similar modeling object, notably Krippner (2013), Wu and Xia{{p}}(2015), and Bauer and Rudebusch (2014) that is typically derived from no-arbitrage conditions embedded in a term structure model for{{p}}nominal interest rates. Our contribution is that we estimate time-variation in the longer-run normal level of the federal funds rate in a{{p}}somewhat more agnostic time-series framework that infers the shadow rate merely by treating nominal interest rates at the ELB as{{p}}censored shadow rates, but without imposing any particular economic structure. As a first step towards our approach the federal funds{{p}}rate at the ELB could also be treated as missing data (without regard for any censoring constraints), which is equivalent to allowing the{{p}}shadow rate to take any value during that period, as has been done, for example, by Tallman and Zaman (2012). In this case, the{{p}}information contained in the federal funds rate during the period of the ELB would be discarded and the shadow rate would be inferred{{p}}merely from the historical co-movements between the federal funds rate and the other macroeconomic variables used in our model.{{p}}Below, we present results from both modeling approaches.{{p}}Estimates of the Longer-Run Level of the Real Federal Funds Rate{{p}}Figure 1 shows our estimate of the expected long-run real federal funds rate when we impose the restriction that the shadow rate be{{p}}less than the zero during the period of the ELB. Panel A shows estimates that, at each date, use information up to that date (we refer to{{p}}these as filtered estimates). Panel B shows estimates that use the entire data sample (we refer to these as smoothed estimates).{{p}}Nevertheless, as the figure shows, we find some evidence of a decline in the expected longer-run federal funds rate using either the{{p}}filtered or the smoothed estimates.{{p}}Figure 1: Expected Long-Run Real Federal Funds Rate{{p}} Note: At each date, filtered estimates use data up through that date to estimate the parameters of the model and the level of the long-run real federal funds{{p}}rate. Smoothed estimates use data through the entire sample to estimate the parameters and the historical level of the long-run real federal funds rate. For the{{p}}results in this figure, we impose that the shadow rate be less than zero during the period of the ELB. Shaded regions are 50 and 90 percent uncertainty bands.{{p}}Estimated trends are expressed in annual percentage terms.{{p}} FRB: FEDS Notes: The Expected Real Interest Rate in the Long Run: Ti...{{p}}2 of 6 2/10/2016 8:15 AM{{p}}Accessible Version{{p}}The uncertainty bands surrounding our estimates are wide, indicating uncertainty about both the level of and changes in the expected{{p}}longer-run real federal funds rate is large. These results are reminiscent of results reported by Hamilton and others (2015), Kiley (2015),{{p}}and Lubik and Matthes (2015) who also report large uncertainty bands surrounding estimates of the longer-run real rate.{{p}}An important feature of our estimated trend in real interest rates is that any decline began well-before the onset of the Great Recession;{{p}}in particular also when considering our filtered estimates that do not condition on future observations from our data set. Our model sees{{p}}the low federal funds rate since 2008 through the lens of co-movement among all of the variables in our model. Moreover, the{{p}}time-varying volatility of the gap components helps the model attribute large changes in unemployment and inflation, like those seen at{{p}}the onset of the global financial crisis, to cyclical fluctuations. This flexibility of our model helps explain why our model sees less{{p}}movement in the trend real interest rate over the past decade than results reported in some other studies, for example Laubach and{{p}}Williams (2015). Instead, the model delivers an estimate of a slow-moving and long-lived decline in the real rate trend that appears to{{p}}have continued through the past few years.{{p}}To ensure that our results are not overly influenced by our shadow rate modeling device, we also conduct our analysis assuming that the{{p}}federal funds rate data are missing whenever the federal funds rate average for a quarter was less than 25 basis points. Figure 2 shows{{p}}the estimated real rate trend under this alternative treatment of the data. As in Figure 1, panels A and B show filtered and smoothed{{p}}estimates. We again find that the estimated real rate trend is surrounded by lots of uncertainty and that any estimated decline in the{{p}}trend began well before the onset of the global financial crisis.{{p}}Figure 2: Expected Long-Run Real Federal Funds Rate, No ELB{{p}} Note: At each date, filtered estimates use data up through that date to estimate the parameters of the model and the level of the long-run real federal funds{{p}}rate. Smoothed estimates use data through the entire sample to estimate the parameters and the historical level of the long-run real federal funds rate. For the{{p}}results in this figure, we do not impose that the shadow rate be less than zero during the period of the ELB. Shaded regions are 50 and 90 percent uncertainty{{p}}bands. Estimated trends are expressed in annual percentage terms.{{p}}Accessible Version{{p}}Figure 3 shows our estimates of the shadow rate. Panel A shows the shadow rate when we impose the censoring constraint which{{p}}requires that the shadow rate be less than zero during the ELB period and panel B shows the shadow rate when we do not impose this{{p}}restriction. While the estimated shadow rates from the two procedures are not identical, they show similar movements over time. This{{p}}indicates that the information contained in the longer-term interest rate, as well as the information from inflation and the unemployment{{p}}rate gap, is informative about shorter-term rates, and that our shadow rate modeling device is unlikely to be driving our results regarding{{p}}the longer-run real federal funds rate.{{p}}Figure 3: Estimated Shadow Rate{{p}} FRB: FEDS Notes: The Expected Real Interest Rate in the Long Run: Ti...{{p}}3 of 6 2/10/2016 8:15 AM{{p}} Note: At each date, the estimates of the shadow rate shown in both panels use data through the entire sample to estimate the parameters and the historical{{p}}level of the shadow rate. Prior to 2008, the shadow rate in our model is equal to the federal funds rate. Shaded regions are 50 and 90 percent uncertainty{{p}}bands. Estimated shadow rates are expressed in annual percentage terms.{{p}}Accessible Version{{p}}Our final figure displays the data we use as inputs to our model estimation, along with the smoothed trends that our model produces.{{p}}Figure 4: Macroeconomic Data and Estimated Trends{{p}} Note: Raw data are shown as the red solid line. At each date, we use data through the entire sample to estimate the parameters and the historical levels of the{{p}}trends shown. Shaded regions are 50 and 90 percent uncertainty bands. Inflation and interest rates, along with their estimated trends, are expressed in annual{{p}}percentage terms. Vertical dotted lines indicate peaks and troughs associated with recession, as dated by the National Bureau of Economic Research,{{p}} .{{p}}Accessible Version{{p}}Conclusions{{p}}Interest rates in the United State have been historically low since 2008. Given the severity of the recession that followed the global{{p}}financial crisis, it is no surprise that interest rates fell. However, it remains to be seen how much of that decline will be long-lasting.{{p}}Our modeling framework allows us to estimate the trend component of the real federal funds rate while filtering out cyclical fluctuations in{{p}} FRB: FEDS Notes: The Expected Real Interest Rate in the Long Run: Ti...{{p}}4 of 6 2/10/2016 8:15 AM{{p}}the real federal funds rate based on its co-movement with other macroeconomic conditions as measured by the CBO's unemployment{{p}}rate gap, inflation, and financial conditions represented by the level of longer-term Treasury yields. As our exercise draws on nominal{{p}}interest rate data, we also account for the period of the ELB by modeling observed nominal interest rates as censored realizations of{{p}}so-called shadow rates, which are hypothetical nominal interest rates implied by our model for the case where interest rates could fall{{p}}below ELB. Our results suggest that any decline in the trend component of real interest rates over recent years can be thought of as a{{p}}continuation of a decline that began years before. However, our results also suggest that estimates of the longer-run expected value of{{p}}the federal funds rate are surrounded by a large amount of uncertainty.{{p}}References{{p}}Bauer, Michael and Glenn Rudebusch, forthcoming, "Monetary Policy Expectations at the Zero Lower Bound," Journal of Money, Credit,{{p}}and Banking.{{p}}Bernanke, Ben S., February 20, 2004, "The Great Moderation," Remarks at the meetings of the Eastern Economic Association,{{p}}Washington, DC.{{p}}Beveridge, Stephen and Charles R. Nelson, 1981, "A New Approach to Decomposition of Economic Time Series into Permanent and{{p}}Transitory Components with Particular Attention to Measurement of the 'Business Cycle'," Journal of Monetary Economics, 7(2), pp.{{p}}151-174.{{p}}Blanchard, Olivier and Stanley Fischer, 1989, March, "Lectures on Macroeconomics," MIT Press, ISBN: 9780262022835.{{p}}Cogley, Timothy, Giorgio E. Primiceri, and Thomas J. Sargent, 2010. "Inflation-Gap Persistence in the US," American Economic Journal:{{p}}Macroeconomics, 2(1): 43-69.{{p}}Garnier, Christine, Elmar Mertens, and Edward Nelson, 2015, September, "Trend Inflation in Advanced Economies," International{{p}}Journal of Central Banking.{{p}}Hamilton, James D., Ethan S. Harris, Jan Hatzius, and Kenneth D. West, 2015, October, "The Equilibrium Real Funds Rate: Past,{{p}}Present, and Future," Hutchins Center on Fiscal & Monetary Policy at Brookings Working Paper #16.{{p}}Kiley, Michael T., 2015, August, "What Can the Data Tell Us About the Equilibrium Real Interest Rate?" Finance and Economics{{p}}Discussion Series, Federal Reserve Board of Governors, 2015-077.{{p}}Krippner, Leo, 2013, August, "A Tractable Framework for Zero Lower Bound Gaussian Term Structure Models," Reserve Bank of New{{p}}Zealand Discussion Paper Series, DP2013/02.{{p}}Laubach, Thomas and John C. Williams, 2015, November, "Measuring the Natural Rate of Interest Redux," Hutchins Center on Fiscal &{{p}}Monetary Policy at Brookings Working Paper #15.{{p}}Lubik, Thomas A. and Christian Matthes, 2015, October. "Calculating the Natural Rate of Interest: A Comparison of Two Alternative{{p}}Approaches." Economic Brief No. 15-10. Federal Reserve Bank of Richmond.{{p}}Mertens, Elmar, forthcoming, "Measuring the Level and Uncertainty of Trend Inflation," Review of Economics and Statistics.{{p}}Rachel, Lukasz and Thomas D. Smith, 2015, December, "Secular Drivers of the Global Real Interest Rate," Bank of England Staff{{p}}Working Paper, No. 571.{{p}}Shephard, Neil, 2016. "Martingale Unobserved Component Models," in Unobserved Components and Time Series Econometrics edited{{p}}by Siem Jan Koopman and Neil Shephard, Oxford University Press.{{p}}Stock, James H. and Mark W. Watson, 2007, February, "Why has U.S. Inflation Become Harder to Forecast?" Journal of Money, Credit,{{p}}and Banking 29(S1), 3-33.{{p}}Stock, James H. and Mark W. Watson, 2010, October, "Modeling Inflation after the Crisis" NBER Working Papers 16488, National{{p}}Bureau of Economic Research.{{p}}Stock, James H. and Mark W. Watson, 2015, June, "Core Inflation and Trend Inflation," NBER Working Papers 21282, National Bureau{{p}}of Economic Research.{{p}}Summers, Lawrence H., 2014, "US Economic Prospects: Secular Stagnation, Hysteresis and the Zero Lower Bound," Business{{p}}Economics, 49(2), pp. 65-73.{{p}}Tallman, Ellis W. and Saeed Zaman, 2012, October. "Where Would the Federal Funds Rate Be, If It Could Be Negative?" Economic{{p}}Commentary No. 2012-15, Federal Reserve Bank of Cleveland.{{p}}Wu, Jing Cynthia and Fan Dora Xia, forthcoming, "Measuring the Macroeconomic Impact of Monetary Policy at the Zero Lower Bound,"{{p}}Journal of Money, Credit, and Banking.{{p}}Yellen, Janet L., December 2, 2015. "The Economic Outlook and Monetary Policy," Remarks at the Economic Club of Washington, D.C.{{p}}1. Board of Governors of the Federal Reserve System. The views expressed here are those of the authors and not necessarily the views of the Board of{{p}}Governors, the FOMC, or anyone else associated with the Federal Reserve System. Return to text{{p}} FRB: FEDS Notes: The Expected Real Interest Rate in the Long Run: Ti...{{p}}5 of 6 2/10/2016 8:15 AM{{p}}Accessibility Contact Us Disclaimer Website Policies FOIA PDF Reader{{p}}2. See, for example, Summers (2014) and Rachel and Smith (2015). Return to text{{p}}3. The neutral equilibrium real rate is typically defined as the inflation-adjusted "value of the federal funds rate that would be neither expansionary nor{{p}}contractionary if the economy were operating near its potential" (Yellen, 2015). Since our estimated real-rate trend can only provide a perspective on{{p}}longer-run expectations of the neutral equilibrium real rate, it does not provide an indication about the appropriate stance of monetary policy in the near term.{{p}}Return to text{{p}}4. Implicitly, our model treats the CBO's estimate of the natural rate of unemployment as the (known) trend rate of unemployment. Return to text{{p}}5. The common trend assumption does not restrict long-run forecasts of short- and long-term interest rates to be identical because we additionally estimate an{{p}}average difference between long-term and short-term rates. Return to text{{p}}6. Specifically, we allow for time-varying volatility in three components of the inflation process: trend and cycle plus a serially uncorrelated measurement error{{p}}that serves to filter out the high-frequency variations in headline inflation. Return to text{{p}}7. See . Return to text{{p}}8. See . Return to text{{p}}9. Available at , and , respectively. Return to text{{p}}10. Available at and . Return to text{{p}}Please cite this note as:{{p}}Benjamin K. Johannsen and Elmar Mertens (2016). "The Expected Real Interest Rate in the Long Run: Time Series Evidence with the{{p}}Effective Lower Bound," FEDS Notes. Washington: Board of Governors of the Federal Reserve System, February 9,{{p}}/10.17016/2380-7172.1703.{{p}} Disclaimer: FEDS Notes are articles in which Board economists offer their own views and present analysis on a range of topics in{{p}}economics and finance. These articles are shorter and less technically oriented than FEDS Working Papers.{{p}}Last update: February 9, 2016{{p}}Home | Economic Research & Data{{p}} FRB: FEDS Notes: The Expected Real Interest Rate in the Long Run: Ti...{{p}}6 of 6 2/10/2016 8:15 AM
    Date: 2016–02–09
  20. By: Giovanni Favara; Simon Gilchrist; Kurt F. Lewis; Egon Zakrajsek
    Abstract: Print{{p}}April 8, 2016{{p}}Recession Risk and the Excess Bond Premium1{{p}}Giovanni Favara, Simon Gilchrist, Kurt F. Lewis, and Egon Zakrajšek{{p}}Corporate bond spreads and the slope of the Treasury yield curve (that is, the term spread) are two financial indicators that are{{p}}especially informative about the likelihood of an economic downturn over a medium-term horizon.2 One reason for this purely statistical{{p}}result is that those yield spreads--like all financial asset prices--are forward-looking variables, and thus they contain important{{p}}information about the real economy. For example, under the expectations hypothesis and neglecting term premiums, the term spread is{{p}}a useful summary of the current stance of monetary policy (relative to long-run expectations), which, of course, has an important effect{{p}}on macroeconomic outcomes. Credit spreads, on the other hand, may anticipate future economic activity because they incorporate{{p}}investors' expectations of future corporate defaults, which affect the business sector's profits, employment, and investment.{{p}}A number of recent papers have emphasized that credit spreads, in particular, may help predict economic activity for reasons unrelated{{p}}to default risk.3 In this FEDS Note, we evaluate the information content for recession risk of a component of credit spreads that is not{{p}}directly attributable to expected default risk and thus to news about future cash flows. Specifically, we use a measure of investor{{p}}sentiment or risk appetite in the corporate bond market--the so-called excess bond premium (EBP) introduced by Gilchrist and Zakrajšek{{p}}(2012)--to predict the likelihood of an NBER-dated recession occurring over the next 12 months.{{p}}In the first parts of the note, we document that over the past four decades, the predictive power of credit spreads for economic{{p}}downturns is due entirely to the EBP. According to this measure, credit market sentiment has deteriorated noticeably since the summer{{p}}of 2015. In fact, by the end of February of this year, the EBP-implied probability that the U.S. economy will enter a recession over the{{p}}subsequent 12 months climbed to about 55 percent. Since then, however, recession risks have receded notably, consistent with an{{p}}improvement in broad financial conditions. In the second part of the note, we discuss two economic mechanisms related to investor{{p}}beliefs and the supply of credit that may explain the relationship between the EBP and the real economy.{{p}}The excess bond premium{{p}}In a recent paper, Gilchrist and Zakrajšek (2012) (GZ hereafter) introduce a corporate bond credit spread with a high information content{{p}}for economic activity that is built from the bottom up, using secondary market prices of senior unsecured bonds issued by a large{{p}}representative sample of U.S. non-financial firms. To avoid duration mismatch issues, which can contaminate the information content of{{p}}credit-risk indicators, yield spreads for each underlying corporate security are derived from a synthetic risk-free security that exactly{{p}}mimics the cash flows of that bond. The GZ spread in month t is a simple un-weighted cross-sectional average of credit spreads in that{{p}} month:{{p}}where denotes the number of bonds in month t and is the spread of bond k (a security that is a liability of firm i) in month t.{{p}}The EBP is extracted from the GZ spread by first using a linear regression to remove expected default risk of individual firms from the{{p}}underlying credit spreads. Specifically, the log of the credit spread on bond k is assumed to be linearly related to a firm-specific indicator{{p}}of default and a vector of bond-specific characteristics , according to:{{p}}where is a (log) credit spread pricing error.4 Assuming normally distributed pricing errors, the predicted level of the spread for{{p}}bond k of firm i at time t--the part attributable to expected default risk--is then given by:{{p}}where denotes the vector of estimated parameters and is the estimate of the conditional variance of pricing errors. The part of{{p}} FRB: FEDS Notes: Recession Risk and the Excess Bond Premium{{p}}1 of 6 4/8/2016 2:16 PM{{p}}the GZ spread that is directly attributable to expected default risk is given by the average of predicted spreads in month t:{{p}}while the EBP is the component of the GZ spread net of expected defaults:{{p}}The above procedure thus decomposes the GZ credit spread into two parts: (1) , a component that captures default risk of{{p}}individual firms; and (2) , a residual component that can be thought of as capturing investor attitudes toward corporate credit{{p}}risk--that is, credit market sentiment. In effect, the EBP tries to capture the variation in the average price of bearing U.S. corporate credit{{p}}risk--above and beyond the compensation that investors in the corporate bond market require for expected defaults. As documented by{{p}}GZ, the EBP is significantly more informative--in both economic and statistical terms--about future economic activity than a component{{p}}of the GZ credit spread that can be directly attributed to expected defaults.{{p}}Panels (a) and (b) Figure 1 show these two credit risk indicators since January 1973, the first available data point. Note that both the GZ{{p}}credit spread and the EBP have increased significantly prior to or during most of the cyclical downturns since the early 1970s. Note also{{p}}that between the summer of 2015 and February of this year, the EBP climbed to levels seen in the early summer of 2008, just a few{{p}}months before the nadir of the financial crisis. However, this pronounced deterioration in credit market sentiment abated appreciably in{{p}}March, a move consistent with the improvement in broad financial conditions.{{p}}Figure 1: Credit Risk Indicators{{p}} FRB: FEDS Notes: Recession Risk and the Excess Bond Premium{{p}}2 of 6 4/8/2016 2:16 PM{{p}} Note: Sample period: monthly data from January 1973 to March 2016. The shaded vertical bars represent the NBER-dated recession.{{p}} Sources: Authors' calculations based on: Center for Research in Security Prices (CRSP); CRSP/Compustat Merged Database, Wharton Research Data{{p}}Services (WRDS), ; and Bank of America Merrill Lynch Bond Indices, used with permission.{{p}}Accessible version{{p}}Predicting NBER-dated recessions{{p}}Following a large literature on estimating recession risks, we use simple probit regressions to estimate the probability that the U.S.{{p}}economy will enter a recession sometime during the next 12 months. As in Gilchrist and Zakrajšek (2012), we focus on the GZ spread,{{p}}the slope of the Treasury yield curve, and the real federal funds rate--two commonly used indicators of the stance of monetary policy--as{{p}}our explanatory variables.5 Our baseline probit regression is given by{{p}}where is a 0/1-indicator variable that equals 1 if and only if there is an NBER-dated recession at some point during months{{p}}t and t+12 (inclusive), is the GZ credit spread, is the term spread, is the real funds rate, and Φ(⋅) denotes the standard{{p}}normal cumulative distribution function.6 Intuitively, the above specification looks at the behavior of these financial indicators at times of{{p}}past NBER-dated recessions and estimates the probability that they are signaling the occurrence of an economic downturn over the next{{p}}year.{{p}}The first column in Table 1 reports the marginal effects of these three financial indicators on the probability of a recession over the{{p}}12-month horizon. Note that all three marginal effects have their expected signs: a widening of credit spreads increases the likelihood of{{p}}an economic downturn over the coming year, as does the flattening of the yield curve--a decline in the term spread--and an increase in{{p}}the real federal funds rate. In economic terms, an increase in the GZ spread of 50 basis points in month t is estimated to increase the{{p}}probability of a recession over subsequent 12 month by about 7 percentage points. A decline in the term spread of the same magnitude{{p}}increases this likelihood by almost 4 percentage point, while a 50 basis point increase in the real funds rate is associated with an{{p}}increase in the probability of a recession over subsequent 12 month of almost 2.5 percentage points.{{p}}Table 1: Financial Indicators as Predictors of Recession Risk{{p}}Explanatory Variables (1) (2) (3){{p}}GZ credit spread 0.140*** . .{{p}}(0.037){{p}}Term spread -0.079** -0.092*** .{{p}}(0.034) (0.029){{p}}Real federal funds rate 0.047** 0.017{{p}}(0.021) (0.016){{p}}Predicted GZ credit spread . -0.018 .{{p}}(0.057){{p}}Excess bond premium . 0.300*** 0.327***{{p}}(0.055) (0.075){{p}}Pseudo R2 0.426 0.527 0.288{{p}} Note: Sample period: monthly data from January 1973 to March 2016 (T = 519). The dependent variable in each specification is a 0/1-indicator variable that{{p}}equals 1 if and only if there is an NBER-dated recession at some point between month t and month t+12. The entries in the table denote the marginal effect of{{p}}the specified explanatory variable on the probability of recession over the 12-month horizon based on the coefficients from the probit regression. All{{p}}specifications include a constant (not reported). The Newey-West standard errors are reported in parentheses; * ; ** ; and *** . The{{p}}pseudo R2 is computed according to McKelvey and Zavoina (1975).{{p}}In column 2, we zoom in on the information content of the GZ credit spread by allowing its two components to enter into the regression{{p}}separately. That is, we estimate{{p}} FRB: FEDS Notes: Recession Risk and the Excess Bond Premium{{p}}3 of 6 4/8/2016 2:16 PM{{p}}These estimates indicate that the predictive content of the GZ spread is due entirely to the EBP--the marginal effect of the default-risk{{p}}component of the GZ spread is statistically and economically indistinguishable from zero. By contrast, a 50 basis point increase in the{{p}}EBP in month t is estimated to boost the likelihood of an economic downturn over the subsequent 12 months by 15 percentage points.{{p}}Column 3 reports the estimation results from a probit regression with the EBP as a sole explanatory variable. The marginal effect of the{{p}}EBP stays essentially the same--in both economic and statistical terms--and according to the pseudo R2, the specification involving only{{p}}the EBP has an in-sample goodness-of-fit that is more than one-half of that implied by the model that conditions on all financial{{p}}indicators (column 2).{{p}}To isolate the role of credit market sentiment in U.S. business cycle fluctuations, Figure 2 plots the in-sample fitted probability of a{{p}}recession over the subsequent 12 months, based on the specification reported in column 3. As shown by the solid line, this probability{{p}}has moved up significantly since the summer of last year, when concerns about global growth prospects, centered on China, sparked a{{p}}widespread re-pricing of risky assets, an increase in financial market volatility, and a deterioration in investor sentiment in the United{{p}}States and abroad; in fact, at the end of February, our simple model implied about a 55 percent chance that the U.S. economy will be in{{p}}recession at some point between March 2016 and February 2017.7 The same concerns that triggered the worsening of investor{{p}}sentiment apparently abated in March, and the corresponding EBP-implied odds of the economy falling into a recession over the{{p}}subsequent 12 months moved down significantly.{{p}}Figure 2: Recession Risk and the Excess Bond Premium{{p}} Note: Sample period: monthly data from January 1973 to March 2016. The solid line depicts the in-sample probability of an NBER-dated recession occurring at{{p}}any point over the subsequent 12 months, implied by the probit specification in column 4 of Table 1; the dotted black line denotes the unconditional probability{{p}}of entering into a recession at any point over the subsequent 12 months. Shaded vertical bars represent the actual NBER-dated recessions.{{p}} Sources: Authors' calculations (see the text and notes to Figure 1 for details).{{p}}Accessible version{{p}}Although this simple model fits the data quite well, it nevertheless produces some--albeit only a few--false negative inferences (failing to{{p}}predict recessions that did occur) and false positive predictions (predicting recessions that did not happen) over the past four decades.{{p}}The most notable false positive prediction in our sample occurred in 2002, when credit spreads and the EBP increased sharply in{{p}}response to a slew of corporate accounting scandals that led to the bankruptcy of several large firms, but the economy did not slip into a{{p}}recession.{{p}}Interpretation{{p}}The results reported above indicate that the EBP provides a timely and useful leading indicator of economic downturns. In the remainder{{p}}of the note, we briefly discuss the economic mechanisms through which fluctuations in credit market sentiment may affect the real{{p}}economy. There are at least two potential mechanisms, which are not necessarily mutually exclusive.{{p}}One possible mechanism linking investor sentiment and the real economy is related to the way investors update their beliefs in light of{{p}}incoming data. In particular, investors may over-react to the most recent news and thus assign excessive weight to future outcomes that{{p}}have become more likely in view of recent data. For example, after a few years of economic expansion, investors may become{{p}}complacent about default risk, an attitude leading to a compression in credit spreads, a loosening of other credit terms and standards,{{p}}and a surge of issuance of credit to very risky borrowers. In such an environment, the sudden arrival of a string of unfavorable economic{{p}}news may lead investors to revise disproportionally their assessment of recession risk, thus amplifying the widening in credit spreads.{{p}}This reasoning implies that investor psychology can itself be a cause of volatility in credit and investment, even in the absence of{{p}}significant changes in economic fundamentals.8{{p}}Another possible mechanism linking fluctuations in credit market sentiment to economic outcomes is related to changes in the supply of{{p}}credit. Large unlevered institutions such as mutual funds, insurance companies, and pension funds have become in recent years the{{p}} FRB: FEDS Notes: Recession Risk and the Excess Bond Premium{{p}}4 of 6 4/8/2016 2:16 PM{{p}}main investors in the corporate bond market. These institutions effectively act as a marginal investor in a wide range of financial{{p}}markets.9 To the extent that real and financial disturbances affect their willingness or ability to fund the provision of new credit, the{{p}}resulting tighter financial conditions may exert a significant drag on future economic growth, dynamics consistent with the standard{{p}}financial accelerator mechanisms emphasized by Bernanke and Gertler (1989), Kiyotaki and Moore (1997), and Bernanke, Gertler and{{p}}Gilchrist (1999).10{{p}}References{{p}}Adrian, Tobias, Erkko Etula, and Tyler Muir. 2014. "Financial Intermediaries and the Cross-Section of Asset Returns." Journal of Finance{{p}}69 (6): 2557-2596.{{p}}Ang, Andrew, Monika Piazzesi, and Min Wei. 2006. "What Does the Yield Curve Tell Us About GDP Growth?" Journal of Econometrics{{p}}131 (1): 359-403.{{p}}Barberis, Nicholas, Andrei Shleifer, and Robert Vishny. 1998. "A Model of Investor Sentiment." Journal of Financial Economics 49:{{p}}307-343.{{p}}Bernanke, Ben, and Mark Gertler. 1989. "Agency Costs, Net Worth, and Business Fluctuations." American Economic Review 79 (1):{{p}}14-31.{{p}}Bernanke, Ben, Mark Gertler, and Simon Gilchrist. 1999. "The Financial Accelerator in a Quantitative Business Cycle Framework." In{{p}}Handbook of Macroeconomics, eds. Michael Woodford and John Taylor, 1341-1393.{{p}}Bleaney, Michael, Paul Mizen, and Veronica Veleanu. Forthcoming. "Bond Spreads and Economic Activity in Eight European{{p}}Economies." Economic Journal.{{p}}Bordalo, Pedro, Nicola Gennaioli, and Andrei Shleifer. 2015. "Diagnostic Expectations and Credit Cycles (PDF)." Working Paper.{{p}}Estrella, Arturo, and Frederic S. Mishkin. 1998. "Predicting U.S. Recessions: Financial Variables As Leading Indicators." Review of{{p}}Economics and Statistics 80 (1): 45-61.{{p}}Estrella, Arturo, and Gikas A. Hardouvelis. 1991. "The Term Structure as a Predictor of Real Economic Activity." Journal of Finance 46{{p}} (2): 555–576.{{p}}Faust, Jon, Simon Gilchrist, Jonathan H. Wright, and and Egon Zakrajšek. 2013. "Credit Spreads as Predictors of Real-Time Economic{{p}} Activity: A Bayesian Model-Averaging Approach." Review of Economics and Statistics 95 (5): 1501–1519.{{p}}Gertler, Mark, and Cara S. Lown. 1999. "The Information in the High-Yield Bond Spread for the Business Cycle: Evidence and Some{{p}}Implications." Oxford Review of Economic Policy 15 (3): 132-150.{{p}}Gilchrist, Simon, and Benoît Mojon. Forthcoming. "Credit Risk in the Euro Area." Economic Journal.{{p}}Gilchrist, Simon, and Egon Zakrajšek. 2012. "Credit Spreads and Business Cycle Fluctuations." American Economic Review 102 (4):{{p}}1692-1720.{{p}}Gilchrist, Simon, Vladimir Yankov, and Egon Zakrajšek. 2009. "Credit Market Shocks and Economic Fluctuations: Evidence From{{p}}Corporate Bond and Stock Markets." Journal of Monetary Economics 56 (4): 471–493.{{p}}Gourieroux, C., A. Monfront, and A Trognon. 1984. "Estimation and Test in Probit Models with Serial Correlation." In Alternative{{p}}Approaches to Time Series Analysis. Facultes Universitaires Saint-Louis.{{p}}Greenwood, Robin, and Samuel G. Hanson. 2013. "Issuer Quality and Corporate Bond Returns." Review of Financial Studies 26 (6):{{p}}1483–1525.{{p}}He, Zhiguo, and Arvind Krishnamurthy. 2013. "Intermediary Asset Pricing." American Economic Review 103 (2): 732–770.{{p}}Kiyotaki, Nobuhiro, and John Moore. 1997. "Credit Cycles." Journal of Political Economy 105 (2): 211-248.{{p}}Krishnamurthy, Arvind, and Tyler Muir. 2015. "Credit Spreads and the Severity of Financial Crises (PDF)." Working Paper..{{p}}López-Salido, David, Jeremy C. Stein, and Egon Zakrajšek. 2016. "Credit-Market Sentiment and the Business Cycle." NBER Working{{p}}Paper 21879.{{p}}Poirier, Dale J., and Paul A. Ruud. 1988. "Probit with Dependent Observations." Review of Economic Studies 55 (4): 593-614.{{p}}Rabin, Matthew, and Dimitri Vayanos. 2010. "The Gambler's and Hot-Hand Fallacies: Theory and Applications." Review of Economic{{p}}Studies 77 (2): 730-778.{{p}}Stock, James H., and Mark W. Watson. 2003. "Forecasting Output and Inflation: The Role of Asset Prices." Journal of Economic{{p}}Literature 41 (3): 788–829.{{p}}Wright, Jonathan H. 2006. "The Yield Curve and Predicting Recessions (PDF)." Finance and Economics Discussion Series No. 2006-07,{{p}}Federal Reserve Board, Washingon DC.{{p}} FRB: FEDS Notes: Recession Risk and the Excess Bond Premium{{p}}5 of 6 4/8/2016 2:16 PM{{p}}Accessibility Contact Us Disclaimer Website Policies FOIA PDF Reader{{p}}1. Favara, Lewis, and Zakrajšek: Division of Monetary Affairs, Federal Reserve Board. Gilchrist: Department of Economics Boston University and NBER.{{p}}Return to text{{p}}2. Indeed, considerable empirical evidence suggests that these yield spreads may be the clearest aggregators of information on incipient recessions. See{{p}}Estrella and Hardouvelis (1991); Estrella and Mishkin (1996, 1998); Stock and Watson (2003); Ang, Piazzesi, and Wei (2006); and Wright (2006) for evidence{{p}}on the term spread. The predictive content of corporate bond spreads for U.S. real economic activity is documented by Gertler and Lown (1999); Gilchrist,{{p}}Yankov, and Zakrajšek (2009); Gilchrist and Zakrajšek (2012); and Faust, Gilchrist, Wright and Zakrajšek (2013); and for the euro area by Gilchrist and Mojon{{p}}(2014) and Bleaney, Mizen, and Veleanu (2016). Return to text{{p}}3. Gilchrist and Zakrajšek (2012); Greenwood and Hanson (2013); Krishnamurthy and Muir (2015); and Lopez-Salido, Stein, and Zakrajšek (2016). Return to{{p}}text{{p}}4. Gilchrist and Zakrajšek (2012) use the "distance-to-default," a default-risk indicator based on the firm's equity valuations and leverage to capture the{{p}}likelihood of default over the near-term horizon; in addition to this market-based assessment of default risk, their credit spread pricing regression also includes{{p}}bond-specific credit ratings, which capture the "through-the-cycle" information about the firm's creditworthiness. These credit risk factors account for about 70{{p}}percent of the variation in bond-level credit spreads over the sample period. In addition, their empirical methodology controls for the call-option effect that is{{p}}embedded in most corporate bonds. Return to text{{p}}5. The term spread is defined as the difference between the yield on the 10-year Treasury note and the 3-month Treasury bill rate. The real federal funds rate{{p}}in month t is defined as the average effective federal funds rate in month t less realized inflation, where realized inflation is given by the log-difference between{{p}}the core PCE price index in month t-1 and its lagged value a year earlier. Return to text{{p}}6. To take into account the serial correlation of the error term induced by the overlapping nature of these forecasts, our statistical inference is based on the{{p}}standard Newey-West estimator of the asymptotic covariance matrix of the model parameters. As shown by Gourieroux, Montfort, and Trongnon (1984) and{{p}}Poirier and Ruud (1988), the resulting pseudo-maximum-likelihood estimates of the model parameters are consistent under quite general (weak) forms of{{p}}serial correlation, though they are inconsistent if the error term exhibits conditional heteroskedasticity. It is worth noting that we obtain qualitatively and{{p}}quantitatively very similar results using a linear probability model. Return to text{{p}}7. As a benchmark, the unconditional probability of entering into a recession at any point over the subsequent 12 months, the dashed line in Figure 2, is about{{p}}30 percent over our sample period. Return to text{{p}}8. Barberis, Shleifer, and Vishny (1998), Rabin and Vayanos (2010), and Bordalo, Gennaioli, and Shleifer (2015) discuss psychological models of investor{{p}}confidence, in which overly-extrapolative expectations may lead to credit and business cycles, even without changes in economic fundamentals. Return to text{{p}}9. He and Krishnamurthy (2013) and Adrian, Etula, and Muir (2014) argue that the wealth of financial intermediaries is an important factor for pricing various{{p}}types of credit risk. Return to text{{p}}10. Lopez-Salido, Stein and Zakrajšek (2016) document the effects of credit market sentiment on economic growth vis-à-vis changes in the supply of credit.{{p}}Return to text{{p}}Please cite this note as:{{p}}Favara, Giovanni, Simon Gilchrist, Kurt F. Lewis, and Egon Zakrajsek (2016). "Recession Risk and the Excess Bond Premium," FEDS{{p}}Notes. Washington: Board of Governors of the Federal Reserve System, April 8, 2016,{{p}} Disclaimer: FEDS Notes are articles in which Board economists offer their own views and present analysis on a range of topics in{{p}}economics and finance. These articles are shorter and less technically oriented than FEDS Working Papers.{{p}}Last update: April 8, 2016{{p}}Home | Economic Research & Data{{p}} FRB: FEDS Notes: Recession Risk and the Excess Bond Premium{{p}}6 of 6 4/8/2016 2:16 PM
    Date: 2016–04–08
  21. By: Seneca, Martin (Bank of England)
    Abstract: Large risk shocks give rise to cost-push effects in the canonical New Keynesian model. At the same time, monetary policy becomes less effective. Therefore, stochastic volatility introduces occasional trade-offs for monetary policy between inflation and output gap stabilisation. The cost-push effects operate through expectational responses to the interaction between shock volatility and the zero lower bound (ZLB) on interest rates. Optimal monetary policy calls for potentially sharp reductions in the interest rate when risk is elevated, even if this risk never materialises. Close to the ZLB, small risk shocks become ‘large’ in this sense. If policy is initially constrained by the ZLB, lift-off is optimally delayed when risk increases.
    Keywords: Risk shocks; uncertainty; zero lower bound on interest rates; optimal monetary policy
    JEL: E52 E58
    Date: 2016–08–11
  22. By: Richard Dennis (University of Glasgow (E-mail:
    Abstract: Many central banks in developed countries have had very low policy rates for quite some time. A growing number are experimenting with official rates that are negative. We develop a New Keynesian model in which the zero lower bound (ZLB) on nominal interest rates is imposed as an occasionally binding constraint and use this model to examine the duration of ZLB episodes. In addition, we show that capital accumulation and capital adjustment costs can raise significantly the length of time an economy spends at the ZLB, as does the conduct of monetary policy. We identify anticipation effects that make the ZLB more likely to bind and we show that allowing negative nominal interest rates shortens average durations, but only by about one quarter.
    Keywords: Monetary policy, zero lower bound, New Keynesian
    JEL: E3 E4 E5
    Date: 2016–08
  23. By: Mary Amiti; Tyler Bodine-Smith; Michele Cavallo; Logan T. Lewis
    Abstract: July 2, 2015{{p}}Did the West Coast Port Dispute Contribute to the First-Quarter GDP Slowdown?{{p}}Mary Amiti , Tyler Bodine-Smith, Michele Cavallo, and Logan Lewis{{p}}The decline in U.S. GDP of 0.2 percent in the first quarter of 2015 was much larger than market analysts expected, with net exports{{p}}subtracting a staggering 1.9 percentage points (seasonally adjusted annualized rate). A range of factors is being discussed in policy{{p}}circles to try to understand what contributed to this decline. Factors such as the strong U.S. dollar and weak foreign demand are usually{{p}}incorporated in forecasters' models. However, the effects of unusual events such as extremely cold weather and labor disputes are more{{p}}difficult to quantify in standard models. In this post, we examine how the labor dispute at the West Coast ports, which began in the{{p}}middle of 2014, might have affected GDP growth. Although the dispute started as early as July 2014, major disruptions to international{{p}}trade did not surface until 2015:Q1. By that time, export and import growth through the West Coast ports in the first quarter were 14{{p}}percentage points to 20 percentage points lower than growth through other ports.{{p}}The West Coast dockworkers' labor contract expired on July 1, 2014. Following months of negotiations, port congestion increased in late{{p}}October amid allegations of a labor slowdown. The situation reached a boiling point in February as port management largely suspended{{p}}operations over a long weekend. Finally, on February 20, labor and management reached a tentative agreement, and port congestion{{p}}started to ease over subsequent weeks.{{p}}In order to gauge the effect of the West Coast dispute on U.S. international trade, we examine import and export shipments across{{p}}various modes of transportation. Goods cross U.S. borders via sea, air, and land (such as trucks crossing Mexico or Canada borders).{{p}}We label the ports that were directly affected by the dispute as "West Coast ports"; these include all major seaports in California,{{p}}Oregon, and Washington State. As for the rest of the ports, we label them as "other."{{p}}We can start to see the potential impact of the dispute from the chart below, which shows that 20 percent of U.S. nonoil merchandise{{p}}imports arrive through the West Coast ports and 10 percent of nonoil merchandise exports leave through them. We focus on exports and{{p}}imports of nominal nonoil merchandise, which account for 8.5 percent and 12 percent of U.S. GDP, respectively.{{p}}Figure 1. Trade Shares by Mode of Transport{{p}} Source: U.S. Census Bureau{{p}} Notes: Data reflect trade in nominal nonoil goods for 2014. West Coast ports include all major seaports in California, Oregon, and Washington{{p}}State.{{p}}Accessible version{{p}}The dispute had the most bite in the first quarter, with imports and exports through the West Coast ports plunging. From the table below,{{p}}we see that exports via West Coast ports fell 20.5 percent in Q1, while imports fell 9 percent. These are substantially larger declines{{p}}relative to previous quarters and bigger declines than in shipments through any other mode of transportation.{{p}} FRB: IFDP Notes: Did the West Coast Port Dispute Contribute to the Fir...{{p}}1 of 4 7/7/2015 11:26 AM{{p}}Table 1. 2015:Q1 Growth in Nonoil Imports and Exports{{p}}Mode of Transport{{p}}Air Land West Other Total{{p}}Imports % Change -1.4 1.7 -8.9 8.4 0.3{{p}}Share 0.3 0.3 0.2 0.2{{p}}ppt contribution -0.4 0.5 -1.8 2.0{{p}}Exports % Change -1.6 -4.0 -20.5 -6.0 -5.2{{p}}Share 0.3 0.4 0.1 0.2{{p}}ppt contribution -0.5 -1.5 -1.8 -1.4{{p}} Source: U.S. Census Bureau and authors' calculations.{{p}} Note: Data are seasonally adjusted nominal nonoil merchandise trade. West indicates goods transported by sea through all major seaports in California,{{p}}Oregon, and Washington State. Other indicates other goods transported by sea.{{p}}Some trade was likely lost irremediably, such as perishable agricultural exports, whereas other goods were rerouted, came in at a later{{p}}date, or were sold to domestic consumers. From the table we see that for exports, shipments through West Coast ports were 14{{p}}percentage points lower than other ports (the difference between the third and fourth columns). For imports, we see disparities between{{p}}West Coast and other ports as high as 17 percentage points. Of course, these observed changes could be driven by compositional{{p}}factors, such as shifts in demand for certain products or other country-specific factors. To check this possibility, we regressed the log{{p}}change in import (and export) nominal values by transportation mode on a West Coast indicator variable and an interaction of this{{p}}indicator with a 2015:Q1 indicator to estimate the average growth difference between West Coast ports and other modes of{{p}}transportation, and to see if the differential widened that quarter. Our analysis controls for detailed product-time effects (for more than{{p}}10,000 product categories) and country-year effects. The results from this analysis support the facts reported above. Specifically, we{{p}}found differential effects for the first quarter between West Coast ports and other ports equivalent to about 20 percent. To see whether{{p}}similar patterns hold for volumes, we reestimated the equations using the physical weight of the shipment rather than the nominal value{{p}}and we found similar results. After controlling for these additional factors, we find a slightly larger differential between West Coast{{p}}shipping and other modes of entry. The larger this estimate, the bigger the decline in West Coast shipments and the larger the{{p}}reallocation of shipments through other ports.{{p}}One takeaway from our analysis is that first-quarter import and export growth through West Coast ports were 14 percentage points to 20{{p}}percentage points lower than growth through other ports. However, it is not straightforward to calculate the magnitude of the effect of the{{p}}dispute on aggregate GDP. If all of the decline in real shipments were reallocated to other modes of transportation then the net effect{{p}}would be zero. A major confounding factor affecting trade during this time is the strong dollar. The appreciation of the dollar reduced the{{p}}demand for exports and increased the demand for imports. We can see this reflected in the table, with exports relatively weak across all{{p}}modes of transportation and imports very strong across all modes other than the West Coast ports; for example, seaborne imports{{p}}through other ports were up 8.4 percent in the first quarter.{{p}}Clearly, lost West Coast exports that were not reallocated to other ports represent a drag on GDP growth. But from a growth accounting{{p}}point of view, lower import growth actually boosts GDP. However, the data indicate that the West Coast import declines during the{{p}}dispute were largely compensated by reallocation to other ports and a March import surge when the dispute was officially over (see the{{p}}next chart). Specifically, nominal imports in March soared, retracing a lot of the decreases posted in January and February. A large{{p}}portion of the rebound was accounted for by imports from Asian economies (China, Japan, and Asian emerging markets), which most{{p}}often enter the United States through West Coast ports. In addition, the March surge reflected a particularly strong jump in imports of{{p}}consumer goods, which tend to be sourced from Asian economies.{{p}}Figure 2. Import of Nonoil Goods by Mode of Transport{{p}} FRB: IFDP Notes: Did the West Coast Port Dispute Contribute to the Fir...{{p}}2 of 4 7/7/2015 11:26 AM{{p}} Source: U.S. Census Bureau{{p}} Notes: West Coast ports include all major seaports in California, Oregon, and Washington State.{{p}}Accessible version{{p}}For exports, the evidence supports the view that the port dispute restrained shipments in the first quarter; most of the decline in exports{{p}}through the West Coast ports does not appear to have been compensated with gains through other transportation modes. One potential{{p}}explanation for why exports were held down was that supply chain issues restrained output, and thus exports for some industries, as{{p}}lower availability of imported intermediate inputs led to a delayed adverse effect on production. To calculate the net loss in exports due{{p}}to the dispute, we work through a counterfactual scenario in which the fall in exports through West Coast ports was commensurate with{{p}}the average fall in exports through other modes of transportation. Estimates from this scenario suggest that the West Coast port{{p}}disruption likely reduced real export growth in the first quarter by 1.5 percentage points. In terms of the contribution to net exports to real{{p}}GDP growth, this would be equivalent to a drag of 0.2 percentage point in the first quarter.{{p}}Figure 3. Exports of Nonoil Goods by Mode of Transport{{p}} Source: U.S. Census Bureau{{p}} Notes: West Coast ports include all major seaports in California, Oregon, and Washington State.{{p}}Accessible version{{p}}In addition to the net exports channel, the dispute could have impacted GDP through consumer and investment spending. On the{{p}}positive side, if goods destined for exporting were added to inventories, then this would boost GDP growth. However, if the dispute{{p}}delayed imports of capital goods and intermediate inputs, this could have led to a loss of manufacturing output and a further loss in U.S.{{p}}exports. In turn, lower exports lead to reduced output and employment of exporting firms, and lower real activity overall through supply{{p}}chains. While these indirect effects might be important, they are more difficult to quantify. Our analysis of the direct effects indicates that{{p}} FRB: IFDP Notes: Did the West Coast Port Dispute Contribute to the Fir...{{p}}3 of 4 7/7/2015 11:26 AM{{p}}Accessibility Contact Us Disclaimer Website Policies FOIA PDF Reader{{p}}lower exports subtracted 0.2 percentage point from GDP growth in the first quarter. It is possible that this loss will be made up later in the{{p}}year, so we will continue to monitor the trade numbers closely.{{p}} Note: This post is running in the Federal Reserve Bank of New York's Liberty Street Economics blog today also.{{p}}Disclaimer{{p}}The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New{{p}}York, the Federal Reserve Board, or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.{{p}}Mary Amiti is an assistant vice president in the Federal Reserve Bank of New York's Research and Statistics Group.{{p}}Tyler Bodine-Smith is a senior research analyst in the Bank's Research and Statistics Group.{{p}}Michele Cavallo is a senior economist in the Division of International Finance at the Federal Reserve Board.{{p}}Logan Lewis is an economist in the Division of International Finance at the Federal Reserve Board.{{p}} Disclaimer: IFDP Notes are articles in which Board economists offer their own views and present analysis on a range of topics in{{p}}economics and finance. These articles are shorter and less technically oriented than IFDP Working Papers.{{p}}Last update: July 2, 2015{{p}}Home | Economic Research & Data{{p}} FRB: IFDP Notes: Did the West Coast Port Dispute Contribute to the Fir...{{p}}4 of 4 7/7/2015 11:26 AM
    Date: 2015–07–02
  24. By: Alan K. Detmeister; Daeus Jorento; Emily Massaro; Ekaterina V. Peneva
    Abstract: June 8, 2015{{p}}Did the Fed's Announcement of an Inflation Objective Influence Expectations?{{p}}Alan Detmeister, Daeus Jorento, Emily Massaro, and Ekaterina Peneva{{p}}Economic theory suggests that inflation expectations are a key determinant of actual inflation. In particular, without shocks from labor{{p}}markets, movements in oil prices, exchange rates or idiosyncratic factors, prices should change at an average pace consistent with{{p}}expectations. Indeed, empirical work attests to their importance: conditioning on long-run inflation expectations from surveys improves{{p}}the accuracy of inflation forecasts.1 Thus, an important question is whether, and how, the Federal Open Market Committee (FOMC) can{{p}}influence inflation expectations.{{p}}Figure 1 shows three survey measures of long-run expected inflation: the expected rate of price change during the next five to ten years{{p}}from the University of Michigan's Survey of Consumers in blue; the expected average Consumer Price Index (CPI) inflation during the{{p}}next ten years from the Philadelphia Fed's Survey of Professional Forecasters, or SPF, in red; and expected Personal Consumption{{p}}Expenditure (PCE) price inflation during the next ten years from the SPF in black.{{p}}Figure 1: Survey Measures of Long-Run Expected Inflation, 1992-Present{{p}}Note. Median responses.{{p}} Sources: University of Michigan, Surveys of Consumers ; Federal Reserve Bank of Philadelphia, Survey of Professional Forecaster (SPF) .{{p}}Accessible version{{p}}As can be seen from the chart, these survey measures have moved little since the late 1990s despite the recent deep recession,{{p}}significant swings in commodity prices, and unprecedented monetary policy actions. The remarkable stability of these measures,{{p}}however, makes it very difficult to figure out what drives them using standard time series regression techniques. For this reason, we look{{p}}at how inflation expectations have changed following a single event--the FOMC's announcement of a 2 percent longer-run objective for{{p}}PCE price inflation in January 2012.{{p}}Starting with the expectations of professional forecasters, in the four quarters following FOMC's announcement, expected PCE price{{p}}inflation during the next 10 years--the solid black line in figure 2--moved from about 1/4 percentage point above the FOMC's objective{{p}}down to the 2 percent objective. In the two years since coming into line with the FOMC's objective, these expectations have remained at{{p}}the 2 percent level.{{p}}Figure 2: Long-Run Expected Inflation from the SPF, 2011-Present{{p}} FRB: FEDS Notes: Did the Fed's Announcement of an Inflation Objectiv...{{p}}1 of 5 6/9/2015 11:07 AM{{p}}Note. Median responses.{{p}} Sources: Federal Reserve Bank of Philadelphia, Survey of Professional Forecaster (SPF) .{{p}}Accessible version{{p}}Arguably, since monetary policy has its greatest influence on inflation in the longer run, removing the initial five years from the ten year{{p}}sample provides a clearer signal of the inflation rate that professional forecasters think is consistent with monetary policy. The black{{p}}dashed line in figure 2 displays professional forecasters' expectations of PCE inflation in years six through ten. These expectations{{p}}trended down more gradually following the announcement, and have just recently reached the FOMC's 2 percent inflation objective. 2 ,3{{p}}Turning to the CPI, expected inflation during the next 10 years--the solid red line--also declined since the announcement, though fairly{{p}}gradually. Given that inflation as measured by the CPI has been slightly higher, on average, than inflation measured by PCE prices, it is{{p}}no surprise that CPI expectations remain above 2 percent. In contrast to the downward movement in the other measures, expected CPI{{p}}inflation in years six through ten--the red dashed line--has shown little net change.{{p}}Taken at face value, the movement in CPI and PCE price inflation expectations provide a little support for the idea that the FOMC's{{p}}announced inflation objective did influence the inflation expectations of professional forecasters, who probably follow FOMC statements{{p}}fairly closely.4{{p}}On the other hand, there is little evidence that households were influenced by the FOMC's announcement. As shown by the blue line in{{p}}figure 1, expected inflation during the next five to ten years from the Michigan survey continued to move sideways after early 2012. It is{{p}}worth noting that Michigan survey does not specify a particular price measure, and the time horizon is less explicit than in the SPF.{{p}}Examining individual responses from the SPF and Michigan survey provides further support for the idea that professional forecasters{{p}}were influenced by the FOMC's announcement, but households were not. As can be seen by comparing the blue and orange bars in{{p}}figure 3, the expectations of professional forecasters of PCE price inflation during the next 10 years became more concentrated around{{p}}2 percent in the three years following the FOMC's January 2012 announcement compared to the three years prior to the announcement.{{p}}Both professional forecasters with very low inflation expectations and very high inflation expectations have become less common, and{{p}}the standard deviation of across participants' responses declined from an average of 0.62 percentage points in the surveys during three{{p}}years prior to the announcement to 0.40 percentage points in the surveys during the three years since. 5 By contrast, the distribution of{{p}}long-run Michigan inflation expectations--figure 4--reveals little if any change in consumer inflation expectations following the{{p}}announcement of a longer-run inflation objective.{{p}}Figure 3: Distribution of SPF Expectations for 10-year PCE Inflation{{p}} FRB: FEDS Notes: Did the Fed's Announcement of an Inflation Objectiv...{{p}}2 of 5 6/9/2015 11:07 AM{{p}} Sources: Authors' calculations using data from Federal Reserve Bank of Philadelphia, Survey of Professional Forecaster (SPF){{p}}Accessible version{{p}}Figure 4: Distribution of Michigan Expectations for 5- to 10-Year Inflation{{p}} Sources: Authors' calculations using data from University of Michigan, Surveys of Consumers .{{p}}Accessible version{{p}}As yet another way of describing how longer-term inflation expectations have changed over time, the next two figures plot annual time{{p}}series of the fraction of SPF and Michigan survey respondents with a long-term inflation expectation of 2 percent. The average share of{{p}}professional forecasters who expected exactly 2 percent PCE inflation during the next 10 years--figure 5--rose noticeably from the three{{p}}years prior to the three years following the FOMC's announcement, though the share after the announcement is no higher than it was in{{p}}2007 and 2008. This increase in the share is statistically significant.6 At the same time, the average share of consumers in the Michigan{{p}}survey who expected 2 percent inflation over the longer term--figure 6--changed little, with the difference being neither economically nor{{p}}statistically significant.7{{p}}Figure 5: Share of SPF Respondents Who Expect Exactly 2 Percent PCE Inflation Over the Next 10 Years{{p}} FRB: FEDS Notes: Did the Fed's Announcement of an Inflation Objectiv...{{p}}3 of 5 6/9/2015 11:07 AM{{p}} Sources: Authors' calculations using data from Federal Reserve Bank of Philadelphia, Survey of Professional Forecaster (SPF) .{{p}}Accessible version{{p}}Figure 6: Share of Michigan Survey Respondents Who Expect 2 Percent Inflation Over the Next 5 to 10 Years{{p}} Sources: Authors' calculations using data from University of Michigan, Surveys of Consumers .{{p}}Accessible version{{p}}To conclude, the data suggest that the FOMC's announcement of an explicit inflation objective had some effect on professional{{p}}forecasters' long-run inflation expectations, but not on households' expectations. Admittedly, inflation expectations of professional{{p}}forecasters did not immediately jump to the FOMC's objective, so it is not clear just how much of change in professional forecasters'{{p}}expectations can be attributed to the FOMC's announcement versus other factors, such as a general reduction in uncertainty as{{p}}economic conditions improved and actual inflation remained moderate. That said, given that the decline in the SPF inflation{{p}}expectations, the tightening of the distribution, and the increase in the share expecting exactly 2 percent inflation started around the time{{p}}of the announcement, it is likely the FOMC's announcement influenced the views of professional forecasters. We cannot say the{{p}}FOMC's announcement had the same influence on households.{{p}} References:{{p}}Binder, Carola, "Fed Speak on Main Street," working paper (2014).{{p}}Clark, Todd E., and Taeyoung Doh, "Evaluating Alternative Models of Trend Inflation," International Journal of Forecasting, Volume 30,{{p}}Issue 3 (2014), 426-448.{{p}} FRB: FEDS Notes: Did the Fed's Announcement of an Inflation Objectiv...{{p}}4 of 5 6/9/2015 11:07 AM{{p}}Accessibility Contact Us Disclaimer Website Policies FOIA PDF Reader{{p}}Faust, Jon, and Jonathan H. Wright, "Forecasting Inflation," in G. Elliott and A. Timmerman (Eds.) Handbook of Economic Forecasting,{{p}}volume 2A. Amsterdam: North Holland (2013).{{p}}Nechio, Fernanda, "Have Long-term Inflation Expectations Declined?" Federal Reserve Bank of San Francisco Economic Letter, no.{{p}}2015-11 (2015).{{p}}Zaman, Saeed, "Improving Inflation Forecasts in the Medium to Long Term," Federal Reserve Bank of Cleveland Economic{{p}}Commentary, no. 2013-16. November 16 (2013){{p}}1. This may be because long-run inflation expectations proxy quite well for the trend in inflation. Including a measure of the trend improves the quality of{{p}}inflation predictions. See, among others, Faust and Wright (2013), Clark and Doh (2014), and Zaman (2013). Return to text{{p}}2. The figure starts in 2011, the time when the SPF implemented a check to confirm that respondents' expectations for inflation 6 to 10 years ahead were{{p}}consistent with their answers about expected inflation during the next 5 and the next 10 years. In order to use longer sample period we focus on SPF{{p}}expectations for 10-year PCE inflation in the rest of the analysis. Return to text{{p}}3. Nechio (2015) finds that the decline in expected PCE inflation in years six through ten in the SPF survey is primarily driven by revised expectations from{{p}}forecasters who overestimated inflation in the aftermath of the Great Recession. Return to text{{p}}4. In addition, in the second quarter of 2012, the SPF panelists were explicitly told that on January 25th, 2012 the FOMC had reached a broad agreement on{{p}}some principles regarding its long-run goals, including that "The committee judges that inflation at the rate of 2 percent, as measured by the annual change in{{p}}the price index for personal consumption expenditures, is most consistent over the longer run with the Federal Reserve's statutory mandate" In the same{{p}}quarter, the Philadelphia Fed's survey of Professional Forecasters included a special question which asked the panelists "...whether their long-run forecast for{{p}}inflation in the price index for personal consumption expenditures (PCE) differs in an economically meaningful way from the FOMC's longer-run goal for{{p}}inflation of 2 percent." About three-quarters of the panelists who answered the special question indicated that their long-run forecasts for PCE inflation did not{{p}}differ from the FOMC's goal in an economically meaningful way. The rest thought that inflation in the long run will exceed 2 percent and the FOMC will not{{p}}achieve its goal. Return to text{{p}}5. Statistical tests strongly reject the equality of the variances in the responses of professional forecasters across the two periods. A similar narrowing of the{{p}}distribution is observed for PCE inflation in years 6 to 10 (not shown). Relatedly, the differences between the upper and lower quartiles for all SPF-based{{p}}measures of longer-run expected inflation (not shown) have declined noticeably since 2012, with the interquartile ranges for the 10-year and six-to-ten-year{{p}}forward measures of expected PCE inflation narrowing to historically low levels. Return to text{{p}}6. We tested for difference in means between the two three-year periods, prior and following the FOMC's inflation objective announcement. The errors were{{p}}corrected for heteroskedasticity and autocorrelations using a Newey-West procedure with 1 lag (the results were not sensitive to using 4 lags). Return to text{{p}}7. Based on analysis of Michigan survey micro data, Binder (2014) concludes that the announcement of an inflation objective has not reached the general{{p}}public and inflation expectations of consumers are weakly anchored. Return to text{{p}} Disclaimer: FEDS Notes are articles in which Board economists offer their own views and present analysis on a range of topics in{{p}}economics and finance. These articles are shorter and less technically oriented than FEDS Working Papers.{{p}}Last update: June 8, 2015{{p}}Home | Economic Research & Data{{p}} FRB: FEDS Notes: Did the Fed's Announcement of an Inflation Objectiv...{{p}}5 of 5 6/9/2015 11:07 AM
    Date: 2015–06–08
  25. By: Ippei Fujiwara (Keio University and The Australian National University (E-mail:; Timothy Kam (The Australian National University (E-mail:; Takeki Sunakawa (The University of Tokyo (E-mail:
    Abstract: We provide new insight on international monetary policy cooperation using a two-country model based on Benigno and Benigno (2006). Assuming symmetry, save for the volatility of (markup) shocks, we show that an incentive feasibility problem exists between the policymakers across national borders: The country faced with a relatively more volatile markup shock has an incentive to deviate from an assumed Cooperation regime to a Non-cooperation regime. More generally, a similar result obtains if countries differ in size. This motivates our study of a history-dependent Sustainable Cooperation regime which is endogenously sustained by a cross-country, state-contingent contract between policymakers. Under the Sustainable Cooperation regime, the responses of inflation and the output gap in both countries are different from the ones under the Cooperation and Non-cooperation regimes reflecting the endogenous welfare redistribution between countries under the state- contingent contract. Such history-contingent welfare redistributions are supported by resource transfers effected through incentive-compatible variations in the terms of trade (or net exports). Such an endogenous cooperative solution may also provide a theoretical rationale for perceived occasional cooperation between national central banks in reality.
    Keywords: Monetary policy cooperation, Sustainable plans, Welfare
    JEL: E52 F41 F42
    Date: 2016–08
  26. By: Timothy S. Hills; Taisuke Nakata; Sebastian Schmidt
    Abstract: Print{{p}}February 12, 2016{{p}}The Risk of Returning to the Effective Lower Bound: An Implication for Inflation Dynamics{{p}}after Lift-Off 1{{p}}Timothy Hills, Taisuke Nakata, and Sebastian Schmidt2{{p}}1. Introduction{{p}}In this note, we analyze an implication of the effective lower bound (ELB) risk—the possibility that adverse shocks will force{{p}}policymakers in the future to lower the policy rate to the ELB—on inflation dynamics after liftoff. The implication we analyze is{{p}}deflationary bias—a phenomenon in which the tail risk induced by the ELB constraint leads inflation to decline under conventional{{p}}monetary policy rules. In particular, our focus is examining how large the deflationary bias is at the economy's risky steady state—the{{p}}point to which the economy eventually converges as headwinds and tailwinds dissipate.{{p}}We first document, briefly, how academic economists, policymakers, and financial market participants assess the ELB risk. We then use{{p}}an empirically rich DSGE (Dynamic Stochastic General Equilibrium) model to analyze how large the deflationary bias induced by the{{p}}ELB risk might be in the U.S. Our baseline simulation suggests that the ELB risk causes inflation to undershoot the target rate of 2{{p}}percent by about 20 basis points at the economy's risky steady state under an empirically plausible specification of monetary policy. We{{p}}find that the deflationary bias induced by the ELB risk can increase to as large as 40 basis points under alternative plausible{{p}}assumptions regarding the long-run equilibrium real rate.{{p}}Overall, our result provides a cautionary tale for policymakers aiming to raise inflation from currently low levels: Even after liftoff, the ELB{{p}}constraint can have enduring adverse effects on inflation through its effects on expectations. The note concludes by briefly discussing{{p}}some implications of this enduring effect of the ELB constraint for monetary policy strategy. The analyses in this note draw heavily on{{p}}Hills, Nakata, and Schmidt (2016).{{p}}2. ELB risk after lift-off{{p}}Academic economists and policymakers have noted that the ELB is not likely to be a one-time event. Ball (2013) argues that, if the{{p}}inflation target remains 2 percent, "the lower bound on interest rates is likely to constrain monetary policy in a large fraction of{{p}}recessions" in the U.S. In its April 2014 WEO, the IMF extensively studies the world wide decline in equilibrium real rates and states that{{p}}the probability of hitting the ELB is likely to increase going forward (IMF (2014)). A renewed interest in the analysis of optimal inflation{{p}}targets and alternative monetary policy frameworks is based on the assumption that the ELB can bind again (Blanchard et al. (2010) and{{p}}Bernanke (2015)).{{p}}Financial market participants also see the ELB binding again in the future as a quantitatively significant possibility. According to a special{{p}}question in the Survey of Primary Dealers on December 2015, a median respondent attached 20 percent probability to the event that the{{p}}federal funds rate returns to the ELB within two years after lift-off.{{p}}3. An implication of the ELB risk: Deflationary bias{{p}}3.1. What is deflationary bias?{{p}}When firms are forward-looking in their pricing decisions, the ELB risk leads inflation to fall below the inflation target at the economy's{{p}}risky steady state, provided that monetary policy is characterized by a standard interest-rate feedback rule. In plain English, this means{{p}}that inflation will return to a level below the inflation target when all headwinds or tailwinds dissipate. This also means that inflation{{p}}fluctuates around that level even in absence of actual ELB episodes. This phenomenon occurs because forward-looking firms base their{{p}}pricing decisions on the expected economic conditions in the future, and the tail risk induced by the ELB constraint lowers the expected{{p}}economic conditions. In the academic literature, this phenomenon is referred to as deflationary bias.3{{p}}3.2. How large is the deflationary bias?{{p}}We use an empirically rich DSGE model calibrated to broadly match key moments of the output gap, inflation, and the federal funds rate{{p}}in the U.S. over the last two decades to get some sense of how large the deflationary bias may be in reality. The model is a standard{{p}}New Keynesian model augmented with consumption habits in the household's preference, sticky wages, and an interest-rate smoothing{{p}}term in the policy rule. There are two shocks---productivity and demand shocks. The main shock that drives fluctuations in this economy{{p}}is the demand shock, which is implemented through time-varying discount rates.4{{p}}The inflation target in the model's interest-rate feedback rule is set to 2 percent. The steady-state discount rate and the growth rate of{{p}}total factor productivity are chosen so that the deterministic steady-state policy rate is 3.75 percent. The effective lower bound on the{{p}}federal funds rate is set to 13 basis points. The list of parameter values is in Hills, Nakata, and Schmidt (2016). The model is solved{{p}}globally using a nonlinear solution method.5{{p}} FRB: FEDS Notes: The Risk of Returning to the Effective Lower Bound...{{p}}1 of 6 2/12/2016 1:20 PM{{p}}The first three rows in Table 1 show the unconditional standard deviations of inflation, the output gap and the nominal interest rate from{{p}}the model and from the data. The standard deviations of the output gap, inflation, and the policy rate in the model are very close to those{{p}}in the data (3.1, 0.42, and 2.34 in the model versus 2.9, 0.52, and 2.34 in the data). The next two rows in Table 1 show the conditional{{p}}mean of the output gap and inflation when the policy rate is at the ELB from the model and from the data. The conditional means of the{{p}}output gap and inflation are somewhat higher and lower than those in the data, but are reasonably close. Finally, the frequency of being{{p}}at the ELB and the expected duration of the ELB episode are 16 percent and 9 quarters in the model, versus 36 percent and 26 quarters{{p}}in the data.6{{p}}Table 1: Key Moments{{p}}Moments Variable Model Data (1995Q3-2015Q2){{p}}St.Dev (X) Output Gap 3.1 2.9{{p}}Inflation 0.42 0.52{{p}}Policy Rate 2.34 2.34{{p}}E(X|ELB) Output Gap -3.4 -4.2{{p}}Inflation 1.18 1.48{{p}}Policy Rate 0.13 0.13{{p}}ELB Frequency 16% 36%{{p}}Expected/Actual Duration 9 quarters 26 quarters{{p}}Table 2 compares the deterministic and risky steady states of inflation, the output gap, and the policy rate. Due to the deflationary bias{{p}}explained earlier, inflation is lower at the risky steady state than at the deterministic steady state Inflation is about 30 basis points lower{{p}}at the risky steady state than the target rate of 2 percent. The policy rate is lower at the risky steady state than the deterministic steady{{p}}state (3.04 percent versus 3.75 percent), as a lower inflation is associated with a lower policy rate in the interest-rate feedback rule.7 The{{p}}expected real rate is lower at the risky steady state, and thus the output gap is positive at the risky steady state, albeit slightly.{{p}}Table 2: The Effects of the ELB Risk on the Steady State{{p}}Inflation Output Gap Policy Rate{{p}}Deterministic Steady State 2 0 3.75{{p}}Risky Steady State 1.71 0.32 3.04{{p}}(wedge) (-0.29) (0.32) (-0.71){{p}}E(X) 1.66 0.29 2.8{{p}}Risky Steady State (No ELB Constraint) 1.88 0.04 3.37{{p}}(wedge) (-0.12) (0.04) (-0.38){{p}}Since the ELB constraint is not the only nonlinear feature of the model, there is some difference between the deterministic and risky{{p}}steady states even in the absence of the ELB constraint, as shown in the last two rows of Table 2. For inflation, the risky steady state is{{p}}1.88 percent in the model without the ELB constraint. The contribution of the ELB risk to the overall deflationary bias at the risky steady{{p}}state is 17 basis points. Thus, the majority of the overall deflationary bias in the model with the ELB constraint comes from the ELB{{p}}constraint, instead of other nonlinearities of the model.{{p}}Note that the risky steady state is conceptually different from the average. Let's take inflation as an example. The risky steady state{{p}}inflation is the point around which inflation fluctuates, while the average inflation is the average of inflation in all states of the economy.{{p}}As shown in Table 2 and Figure 1, the risky steady state inflation is higher than the average inflation in the economy with the ELB{{p}}constraint, as the ELB constraint makes the unconditional distribution of inflation negatively skewed. The observation that the ELB{{p}}constraint pushes down the average inflation below the target by creating asymmetry in the distribution of inflation is intuitive and has{{p}}been well known for a long time (Coenen, Orphanides, and Wieland (2004) and Reifschneider and Williams (2000)). This holds true{{p}}even when price-setters form expectations in a backward-looking manner. The result that the ELB risk lowers the center of the{{p}}distribution below the target is less intuitive and requires that price-setters are forward-looking in forming their expectations.{{p}}Figure 1: Unconditional Distribution of Inflation{{p}} FRB: FEDS Notes: The Risk of Returning to the Effective Lower Bound...{{p}}2 of 6 2/12/2016 1:20 PM{{p}} Note: Authors' calculation as described in Hills, Nakata, and Schmidt (2016){{p}}Accessible version{{p}}To emphasize the fact that the possibility of the ELB is the source of the deflationary bias, we contrast the impulse response functions of{{p}}inflation, output, and the policy rate from the model with those from a perfect-foresight version of the model, which ignores the ELB risk.{{p}}Figure 2 shows the impulse response functions when the initial demand and productivity shocks are chosen so that inflation is 50 basis{{p}}points, the output gap is -7 percent, and the policy rate is at the ELB at period one. Black lines are from the perfect-foresight version of{{p}}the model that abstracts from the ELB risk, while red lines are from the model that correctly takes the ELB risk into account. Under{{p}}perfect-foresight, the policy rate stays at the ELB for 11 quarters and will gradually converge to 3.75 percent. Inflation slowly rises to 2{{p}}percent, and the output gap will converge to zero after some overshooting. On the other hand, when the private sector correctly{{p}}acknowledges the ELB risk, the policy rate stays at the ELB for 13 quarters and returns to its risky steady state of about 3 percent.{{p}}Inflation slowly rises to about 1.7 percent and the output gap remains slightly positive in the long run.{{p}}Figure 2: The Effect of the ELB Risk on Projections{{p}} FRB: FEDS Notes: The Risk of Returning to the Effective Lower Bound...{{p}}3 of 6 2/12/2016 1:20 PM{{p}} Note: Authors' calculation as described in Hills, Nakata, and Schmidt (2016){{p}}Accessible version{{p}}3.3. The long-run equilibrium interest rate and deflationary bias{{p}}The magnitude of the deflationary bias depends importantly on the frequency of the policy rate being at the ELB, which in turn depends{{p}}on the long-run equilibrium policy rate. Figure 3 shows how the magnitude of deflationary bias depends on the (deterministic){{p}}steady-state level of the policy rate. In this figure, we vary the parameter governing the long-run growth rate of labor productivity to{{p}}induce the change in the steady-state policy rate. As demonstrated in the top-right panel, deflationary bias is larger when the{{p}}deterministic steady state policy rate is lower. This is because the probability of hitting the ELB is higher with a lower steady-state policy{{p}}rate, as shown in the top-left panel of the figure. When the deterministic steady state policy rate is 3.35 percent, the probability of being{{p}}at the ELB is 28 percent and inflation is about 50 basis points below the inflation target at the economy's risky steady state. Comparing{{p}}this deflationary bias with that in the model without the ELB constraint, indicated by the dashed black line, shows that the ELB risk{{p}}accounts for about 40 basis points of the overall deflationary bias.8{{p}}Figure 3: Long-Run Policy Rate and Deflationary Bias{{p}} FRB: FEDS Notes: The Risk of Returning to the Effective Lower Bound...{{p}}4 of 6 2/12/2016 1:20 PM{{p}} Note: Authors' calculation as described in Hills, Nakata, and Schmidt (2016){{p}}Accessible version{{p}}4. Concluding remarks{{p}}This note has demonstrated that the deflationary bias induced by the ELB risk is non-trivial in an empirically rich DSGE model calibrated{{p}}to match key features of the U.S. economy. The result that the ELB constraint has enduring effects on the economy even after liftoff has{{p}}important implications for the design of monetary policy. For example, the deflationary bias, while typically ignored in the analyses of{{p}}optimal inflation targets, importantly affects the cost-benefit calculation of changing the inflation target. As another example, Nakata and{{p}}Schmidt (2014) show that the deflationary bias makes it desirable for the central bank to put more weight on the inflation stabilization{{p}}objective, relative to the output stabilization objective.9{{p}}This conclusion is of course conditional on the validity of the model used for the analysis. While the model is a variant of the standard{{p}}model widely used at central banks, there is a continuing debate about the usefulness of this model for policy analysis among{{p}}economists. Thus, caution is needed when drawing policy implications.10{{p}} References:{{p}}Adam, K., and R. Billi (2007): "Discretionary Monetary Policy and the Zero Lower Bound on Nominal Interest Rates," Journal of{{p}}Monetary Economics, 54(3), 728-752.{{p}}Ball, L. M. (2013): "The Case for Four Percent Inflation," Central Bank Review, 13, 17-31.{{p}}Bernanke, B. (2015): "Monetary Policy in the Future," Remarks at the Rethinking Macro Policy III Conference at the IMF, Washington,{{p}}DC.{{p}}Blanchard, O., G. Dell'Ariccia, and P. Mauro (2010): "Rethinking Macroeconomic Policy," Journal of Money, Credit, and Banking, 42(s1),{{p}}199-215.{{p}}Chung, H., E. Herbst, and M. T. Kiley (2014): "Effective Monetary Policy Strategies in New Keynesian Models: A Re-Examination,"{{p}}NBER Working Papers 20611, National Bureau of Economic Research.{{p}}Coenen, G., A. Orphanides, and V. Wieland (2004): "Price Stability and Monetary Policy Effectiveness when Nominal Interest Rates are{{p}}Bounded at Zero," B.E. Journal of Macroeconomics: Advances in Macroeconomics, 4(1).{{p}} FRB: FEDS Notes: The Risk of Returning to the Effective Lower Bound...{{p}}5 of 6 2/12/2016 1:20 PM{{p}}Accessibility Contact Us Disclaimer Website Policies FOIA PDF Reader{{p}}Evans, C., J. Fisher, F. Gourio, and S. Krane (2015): "Risk Management for Monetary Policy Near the Zero Lower Bound," Brookings{{p}}Papers on Economic Activity Conference Draft.{{p}}Hills, T. S., T. Nakata, and S. Schmidt (2016): "The Risky Steady State and the Interest Rate Lower Bound," Finance and Economics{{p}}Discussion Series 2016-009, Board of Governors of the Federal Reserve System (U.S.).{{p}}IMF (2014): "World Economic Outlook" April 2014{{p}}Nakata, T., and S. Schmidt (2014): "Conservatism and Liquidity Traps," Finance and Economics Discussion Series 2014-105, Board of{{p}}Governors of the Federal Reserve System (U.S.).{{p}}Nakov, A. (2008): "Optimal and Simple Monetary Policy Rules with Zero Floor on the Nominal Interest Rate," International Journal of{{p}}Central Banking, 4(2), 73-127.{{p}}Reifschneider, D., and J. C. Williams (2000): "Three Lessons for Monetary Policy in a Low-Inflation Era," Journal of Money, Credit and{{p}}Banking, 32(4), 936-966.{{p}}1. We would like to thank Jean-Phillipe Laforte and John Roberts for their comments. Paul Yoo provided excellent research assistance. The views expressed in{{p}}this note, and all errors and omissions, should be regarded as those solely of the author, and are not necessarily those of the Federal Reserve Board of{{p}}Governors, the Federal Reserve System, or the European Central Bank. Return to text{{p}}2. Timothy Hills is a Ph.D. student at the Stern School of Business, New York University; Taisuke Nakata is an economist at the Board of Governors of the{{p}}Federal Reserve System (Division of Research and Statistics); Sebastian Schmidt is an economist at the European Central Bank (Monetary Policy Research{{p}}Division). Return to text{{p}}3. The implications of deflationary bias for optimal policy have been studied in a stylized New Keynesian model by Adam and Billi (2007), Evans et al. (2015),{{p}}Nakata and Schmidt (2014), and Nakov (2008). Note that the magnitude of the deflationary bias depends on the policy rule in place. Under the price-level{{p}}targeting or optimal commitment policy, the deflationary bias would be smaller as the decline in inflation at the ELB would be smaller. Return to text{{p}}4. See Hills, Nakata, and Schmidt (2016) for more details on the model. Return to text{{p}}5. Due to the computational burden of globally solving dynamic models, economists typically solve monetary DSGE models under the assumption of perfect-foresight.{{p}}That is, after lift-off, the households and firms are assumed to attach zero probability to the event that adverse shocks will push the policy rate back{{p}}to the ELB. Due to curse of dimensionality, the computational burden of globally solving DSGE models increases exponentially with the size of the model. It is{{p}}nearly infeasible to solve more empirically-rich structural models--such as FRB/US, EDO, and SIGMA used at the Federal Reserve--in a reasonable amount of{{p}}time without perfect-foresight assumptions. Return to text{{p}}6. The ELB frequency in the data depends importantly on the sample period. We use the last two decades as our sample because the long-run inflation{{p}}expectations were low and stable during this period. Return to text{{p}}7. As we discuss in Hills, Nakata, and Schmidt (2016), another way to understand why inflation and the policy rate are lower at the risky steady state than at{{p}}the deterministic steady state is to examine the interaction of the Taylor rule with a version of the Fisher relationship modified to account for the effects of risk.{{p}}Return to text{{p}}8. A similar picture emerges if one considers a change in the target rate of inflation or the value of the effective lower bound, as shown in Hills, Nakata, and{{p}}Schmidt (2016). Return to text{{p}}9. In Hills, Nakata, and Schmidt (2016), we study how each parameter in the interest-rate feedback rule affects the magnitude of deflationary bias and show{{p}}that a higher inertia, a higher inflation target, and a lower effective lower bound can reduce the size of the deflationary bias. Return to text{{p}}10. See, for example, Chung, Herbst, and Kiley (2014). Return to text{{p}}Please cite this note as:{{p}}Hills, Timothy, Taisuke Nakata, and Sebastian Schmidt (2016). "The Risk of Returning to the Effective Lower Bound: An Implication for{{p}}Inflation Dynamics after Lift-Off," FEDS Notes. Washington: Board of Governors of the Federal Reserve System, February 12,{{p}}{{p}} Disclaimer: FEDS Notes are articles in which Board economists offer their own views and present analysis on a range of topics in{{p}}economics and finance. These articles are shorter and less technically oriented than FEDS Working Papers.{{p}}Last update: February 12, 2016{{p}}Home | Economic Research & Data{{p}} FRB: FEDS Notes: The Risk of Returning to the Effective Lower Bound...{{p}}6 of 6 2/12/2016 1:20 PM
    Date: 2016–02–12
  27. By: Gregori Galofré-Vilà; Martin McKee; Christopher M. Meissner; David Stuckler
    Abstract: In 1953 the Western Allied powers implemented a radical debt-relief plan that would, in due course, eliminate half of West Germany’s external debt and create a series of favourable debt repayment conditions. The London Debt Agreement (LDA) correlated with West Germany experiencing the highest rate of economic growth recorded in Europe in the 1950s and 1960s. In this paper we examine the economic consequences of this historical episode. We use new data compiled from the monthly reports of the Deutsche Bundesbank from 1948 to the 1960s. These reports not only provide detailed statistics of the German finances, but also a narrative on the evolution of the German economy on a monthly basis. These sources also contain special issues on the LDA, highlighting contemporaries’ interest in the state of German public finances and public opinion on the debt negotiation. We find evidence that debt relief in the LDA spurred economic growth in three main ways: creating fiscal space for public investment; lowering costs of borrowing; and stabilising inflation. Using difference-in-differences regression models comparing pre- and post-LDA years, we find that the LDA was associated with a substantial rise in real per capita social expenditure, in health, education, housing, and economic development, this rise being significantly over and above changes in other types of spending that include military expenditure. We further observe that benchmark yields on long-term debt, an indication of default risk, dropped substantially in West Germany when LDA negotiations began in 1951 and then stabilised at historically low rates after the LDA was ratified. The LDA coincided with new foreign borrowing and investment, which in turn helped promote economic growth. Finally, the German currency, the deutschmark, introduced in 1948, had been highly volatile until 1953, after which time we find it largely stabilised.
    JEL: E62 E65 N44
    Date: 2016–08
  28. By: Claudia R. Sahm
    Abstract: May 1, 2015{{p}}Forecasts of Economic Activity in the Great Recession{{p}}{{p}}Claudia Sahm{{p}}{{p}}Forecasts by the Board staff for economic activity during the Great Recession proved to be overly optimistic on some dimensions, such as GDP, and yet were appropriately pessimistic on other dimensions, such as the GDP gap (the deviation of GDP from potential output).1 This look back on forecasting draws on the newly-released staff forecasts from 2009, as well as Board staff forecasts from 2008.2{{p}}{{p}}The Board staff prepares a multi-year forecast before each FOMC meeting and Figure 1, from left to right, shows the staff forecasts prepared in June 2008, December 2008, June 2009, and December 2009. The solid bars show the change in real GDP for 2008 (blue), 2009 (orange), and 2010 (green) at the time of each staff forecast. The dashed lines show the current estimates for those three years, according to the Bureau of Economic Analysis (BEA).{{p}}Figure 1{{p}}Figure 1: Evolution of Change in Real GDP. See accessible link for data.{{p}}{{p}} Note: Percent changes are from the fourth quarter of the previous year to the fourth quarter of the year indicated. The solid bars combine the GDP estimates from the Bureau of Economic Analysis (BEA) that were available at the date of the forecast with the Board staff forecasts going forward from the published data. The dashed lines are the currently published estimates of GDP from the BEA. The colors of the lines (current) and bars (real-time) match for a particular year.{{p}}{{p}}Accessible version{{p}}{{p}}In the June 2008 forecast--with about a half-year of spending data in hand--the Board staff expected real GDP to increase 1 percent in 2008 (far-left blue bar), 2-1/2 percent in 2009 (far-left orange bar) and 3 percent in 2010 (far-left green bar). Six months later in the December 2008 forecast, well into the financial crisis, the staff views on 2008 (middle-left blue bar) and 2009 (middle-left orange bar) had weakened noticeably. Nevertheless, the decline of 1/2 percent in real GDP for 2008--much of which reflected published spending data--in that forecast was far more optimistic than the current estimate of a 2-3/4 percent decline for 2008 (the dashed blue line). Note, that even in the December 2009 forecast (far-right blue bar), the decline in real GDP in 2008--based on the BEA's first annual revision for that year--was, at 2 percent, still a 3/4 percentage point smaller decline than the current estimate. It was only later that the BEA revised GDP down further to its current estimate. In other words, the staff's overly optimistic forecasts of the recession in figure 1 partly reflect the lags in constructing national statistics, particularly during a business cycle downturn.{{p}}{{p}}And yet, monetary policy with its dual mandate of maximum employment and price stability is unlikely to react directly to GDP growth. Instead, the deviation of GDP from potential output, referred to as the GDP gap, is typically more relevant to monetary policy since it reflects the cyclical position of the economy.3 In fact, standard tools, such as the Taylor rule, for estimating the appropriate level of the federal funds rate depend on cyclical indicators like the GDP gap. Given its usefulness for monetary policy, the GDP gap is a key variable in the Board staff forecast. Figure 2 shows the evolution of the GDP gap in the staff forecasts during the Great Recession.4{{p}}Figure 2{{p}}Figure 2: Evolution of the GDP Gap. See accessible link for data.{{p}}{{p}} Note: The GDP gaps are percent deviations of the level GDP from the level of potential GDP in the fourth quarter of each year. The solid bars are the GDP gaps at the time using the Board staff's GDP and potential GDP. The dashed lines are the GDP gaps using current estimates of GDP from the Bureau of Economic Analysis (BEA) and potential GDP from the Congressional Budget Office (CBO). The colors of the lines (current) and bars (real-time) match for a particular year.{{p}}{{p}}Accessible version{{p}}{{p}}Similar to the forecasts of GDP, the staff's view of the GDP gap worsened noticeably in the December 2008 forecast (the middle-left grouping).5 In that forecast, the Board staff expected that real GDP at the end of 2008 would be 3 percent below potential GDP. This was optimistic relative to the current assessment (using CBO's estimate of potential) that GDP at the end of 2008 was 5 percent below potential.6 However, the forecasts provided by the Board staff in December 2008 for the GDP gap at the end of 2009 and 2010 were much closer to the current assessment. Moreover, in the June 2009 and December 2009 forecasts, the GDP gap for the end of 2010 was decidedly negative and comparable to current assessments. Taken together, since late 2008, the Board staff forecast sent a persistently negative message about the cyclical position of the economy in these years – a message that also seems appropriate given current knowledge.7{{p}}{{p}}This note on GDP growth and the GDP gap has only scratched the surface on the forecast evaluation and there is room for improvement even where the Board staff forecasts were fairly accurate. There are a few takeaways for other such exercises: First, the quality of the information available at the time of the forecast may be an impediment, above and beyond the forecaster's ability to interpret the available information. Second, depending on how the forecasts are used some variables may receive more weight--and thus deserve closer scrutiny--than others. As the saying goes, "all forecasts are wrong, but some are useful." In the end, the staff forecast of economic activity during the Great Recession should be judged by how useful it was to the conduct of monetary policy at the time.{{p}}{{p}}1. Potential output here is the level of output that we would expect to see if prices were able to fully adjust. If the actual level of output is below potential output then we say there is slack in the economy. Unlike actual output, potential output is not directly measured and must be indirectly estimated. For a primer on potential output and its measurement see Basu and Fernald (2009) (PDF). Return to text{{p}}{{p}}2. The Board staff projections are available at (2009): and (2008): Staff forecasts are released with a five-year lag along with the transcripts of FOMC meetings, the FOMC forecasts (the SEP), and other policy materials. Return to text{{p}}{{p}}3. See the recent speech, "Normalizing Monetary Policy: Prospects and Perspectives" by Chair Yellen and the references therein for more on the role of cyclical measures, like the output gap, in monetary policy. Return to text{{p}}{{p}}4. The GDP gaps depicted in the bars are the percent deviation between the Board staff's forecast for GDP and the staff's forecast for potential output at the time. The dashed lines show current estimates of the GDP gaps in the Great Recession, specifically the percent deviation between GDP as now published by the BEA and the Congressional Budget Office's (CBO) estimate of potential output. The CBO estimate of potential GDP is from Table 26 at Note, the Board staff's current estimates of potential at that time are not publicly available but the methodologies are similar. Return to text{{p}}{{p}}5. The fact that Board staff views about the output gap (one measure of the cyclical position of the economy) worsened more than GDP reflects a signal taken from the sharp increase in the unemployment rate, even as the published spending data at the time showed less weakness. Return to text{{p}}{{p}}6. Unlike the estimates of potential output in 2008 and 2009, the Board staff's current estimate of potential output--which may have revised--is not publicly available. For the current estimates of the output gap this note uses the GDP from the BEA and potential output from the CBO. Return to text{{p}}{{p}}7. Another way to gauge the message from the staff at the time is with staff documents that presented the forecast, the Greenbook, or transcripts of the FOMC meetings. As one example, at the December 2008 FOMC meeting (PDF), the staff forecast was described as having a "very substantial output gap" (p. 124). See also a staff presentation at the June 2009 FOMC meeting (PDF) on various measures of resource utilization, including the GDP gap (p.103-105). For a longer perspective, see "Real-Time Properties of the Federal Reserve's Output Gap" by Rochelle Edge and Jeremy Rudd (2012). Return to text{{p}}{{p}} Disclaimer: FEDS Notes are articles in which Board economists offer their own views and present analysis on a range of topics in economics and finance. These articles are shorter and less technically oriented than FEDS Working Papers.{{p}}Search Working Papers{{p}}{{p}}Last update: May 1, 2015
    Date: 2015–05–01
  29. By: Missaka Warusawitharana
    Abstract: October 9, 2014{{p}}The Social Discount Rate in Developing Countries{{p}}{{p}}Missaka Warusawitharana{{p}}{{p}}The "social discount rate" is the interest rate used in cost-benefit analyses of infrastructure and other public projects. As seen from the discussion of the Stern report on climate change (see Stern, 2007, and Nordhaus, 2007), differences in the social discount rate can have substantial implications for evaluating the costs and benefits of public projects. This note proposes a heuristic approach to deriving a social discount rate for developing countries based on the sovereign borrowing rate.{{p}}{{p}}The main methods currently used to calculate the social discount rate are: (1) the social rate of time preference and (2) the social opportunity cost of capital. The first approach is based on the argument that public investment reduces private consumption and thus equates the social discount rate to a rate of time preference, usually estimated with the Ramsey formula.1 The second approach is based on the argument that public investment crowds out private investment one-for-one and, as such, the discount rate is estimated based on the pre-tax real rate of return for private investment, typically estimated using returns to private capital. Based partly on this approach, leading development banks, such as the World Bank and the Asian Development Bank, typically apply a real discount rate in the range of 10 percent to 12 percent when evaluating projects in developing countries (see Zhuang et al., 2007, and Harrison, 2010). Many government agencies in these countries follow such guidelines and apply a similar discount rate when evaluating public projects. Applying such relatively high discount rates implies, for example, that projects requiring a significant upfront cost to realize a flow of benefits over long periods of time may be discouraged.{{p}}{{p}}This note proposes using the real interest rate at which developing countries can borrow as the social discount rate. For instance, one could use a recent average of a sovereign government's cost to borrow in U.S. dollars, adjusted for U.S. inflation rates, to measure the social discount rate for a developing country. A rationale for this measure is that it would significantly correspond to the borrowing cost of the government that would, in most cases, be responsible for funding the project. Thus, using the sovereign borrowing rate as the social discount rate would enable one to match the projected cash inflows from the project to the cash outflows for the government responsible for financing it.2 This approach, in fact, reflects the current practice of most European governments, who link the social discount rate to their borrowing costs. In addition, U.S. government agencies either use a rate based on government borrowing rates or a higher rate obtained from a social opportunity cost of capital calculation (see Office of Management and Budget, 1992).{{p}}{{p}}The proposed method would not have been feasible until recently, as most developing countries were not able to access dollar-denominated sovereign debt markets. Reflecting the increased globalization of financial markets, however, there has been a marked increase in access to sovereign debt markets for developing countries, as detailed in The Economist (2014). Table 1 lists data on recent issuance of dollar-denominated sovereign debt with maturity greater than five years by selected developing countries.3 Many of these countries issued their first such bond during the past few years.{{p}}Table 1: Recent US$ Sovereign Debt Issuance by Developing Countries{{p}}Country Issue Date Yield-to-maturity Amount (US$ millions){{p}}Kenya 6/17/2014 6.88% 1500{{p}}Zambia 4/7/2014 8.63% 1000{{p}}Ivory Coast 7/16/2014 5.63% 750{{p}}Sri Lanka 4/8/2014 5.13% 500{{p}}Pakistan 4/9/2014 8.25% 1000{{p}}Ecuador 6/16/2014 7.95% 2000{{p}}Senegal 7/23/2014 6.25% 500{{p}}Honduras 12/11/2013 8.75% 500{{p}}Gabon 12/5/2013 6.38% 1500{{p}}Bolivia 8/15/2013 6.25% 500{{p}}Nigeria 7/2/2013 6.63% 500{{p}}{{p}}The figures in Table 1 indicate that using sovereign borrowing costs would result in lower social discount rates than are currently applied in developing countries' cost-benefit analyses for public projects. The (nominal) interest rates in Table 1 (labeled "yield-to-maturity") average about 7 percent; after adjusting for U.S. inflation, the implied social discount rate would be on the order of 5 percent. This lower discount rate is similar to that recommended by Lopez (2008) for selected Latin American countries. It should be noted that the low social discount rate implied by the proposed method would rise if sovereign debt yields were to increase notably, possibly due to a change in the global macroeconomic environment or country-specific developments.{{p}}{{p}}Discount rates ought to incorporate a risk premium, since the projected future benefits of a project may not materialize. The use of sovereign borrowing rates implicitly assumes that the risk premium on public infrastructure projects is the same as the premium for the default risk of a country. In contrast, the social opportunity cost of capital approach assumes that the risk premium on public infrastructure projects is the same as that on private investment. But the private risk premium on private project is likely too high for public sector projects, due to the fact that it involves compensation for factors (for instance, weak institutions) that may not apply for the consideration of public projects.{{p}}{{p}}Another consideration that points toward using a moderate social discount rate in developing countries is that current practices might tend to underestimate some of the potential benefits of many infrastructure projects. For instance, public infrastructure projects frequently are expected to generate positive externalities (see Fernald, 1993), which would potentially amplify the returns from the infrastructure investment compared with current estimation practices. These externalities are hard to estimate, and are typically overlooked in a traditional cost benefit analysis.{{p}}{{p}}To summarize: this note argues for using real sovereign borrowing rates as the social discount rate for evaluating public projects in developing countries. Compared with standard approaches, such an approach would currently result in a lower discount rate than currently applied, potentially leading to greater public infrastructure investments in developing countries. If carried out wisely, such investments may help boost living standards for many people in these countries.{{p}}{{p}}{{p}}References{{p}}Fernald, John, 1999, Roads to prosperity? Assessing the link between public capital and productivity, American Economic Review, volume 89(3), pages 619-638.{{p}}{{p}}Harrison, Mark, 2010, Valuing the future: The social discount rate in the cost-benefit analysis, Visiting Researcher Paper, Australian Government Productivity Commission.{{p}}{{p}}Nordhaus, William, 2007, The Stern Review on the economics of climate change, Journal of Economic Literature, volume 45, pages 686-702.{{p}}{{p}}Office of Management and Budget, 1992, Circular no. A-94, Guidelines and discount rates for Benefit-Cost Analysis of Federal Programs. Washington, D.C.{{p}}{{p}}Stern, Nicholas, 2007, The Economics of Climate Change: The Stern Review, Cambridge University Press Cambridge, UK.{{p}}{{p}}The Economist, 2014, Frontier markets: Wedge beyond the edge, The Economist, April 5.{{p}}{{p}}Zhuang Juzhong, Zhihong Liang, Tun Lin, and Franklin De Guzman, 2007, Theory and practice in the choice of social discount rate for cost-benefit analysis: A survey, Asian Development Bank ERD Working Paper #94.{{p}}{{p}}1. The Ramsey formula implies that the social rate of time preference equals the intertemporal discount rate plus the consumption growth rate times the elasticity of the marginal utility of consumption. Return to text{{p}}{{p}}2. This ignores the potential foreign currency risk faced by the borrowing government. This risk could, in principle, be hedged using financial instruments, though such hedging would need to be included in the total cost of the project. Return to text{{p}}{{p}}3. I thank Wenxin Du and Rick Ogden for helping provide the data. Return to text{{p}}{{p}} Disclaimer: FEDS Notes are articles in which Board economists offer their own views and present analysis on a range of topics in economics and finance. These articles are shorter and less technically oriented than FEDS Working Papers.{{p}}Search Working Papers{{p}}{{p}}{{p}}Last update: October 9, 2014
    Date: 2014–10–09
  30. By: Francesco Bianchi; Martin Lettau; Sydney C. Ludvigson
    Abstract: This paper presents evidence of infrequent shifts, or "breaks," in the mean of the consumption-wealth variable cay_{t}, an asset market valuation ratio driven by fluctuations in stock market wealth relative to economic fundamentals. Conventional estimates of cay_{t}, which presume a constant mean, display increasing persistence over the sample. We introduce a Markov-switching version of cay_{t} that adjusts for infrequent shifts in its mean. The Markov-switching cay_{t}, denoted cay_{t}^{MS}, is less persistent and has superior forecasting power for excess stock market returns compared to the conventional estimate. Evidence from a Markov-switching VAR shows that these low frequency swings in post-war asset valuation are strongly associated with low frequency swings in the long-run expected value of the Federal Reserve's primary policy rate, with low expected values for the real federal funds rate associated with high asset valuations, and vice versa. By contrast, there is no evidence that the infrequent shifts to high asset valuations and low policy rates are associated with higher expected economic growth or lower economic uncertainty; indeed the opposite is true.
    JEL: E02 E4 E52 G12
    Date: 2016–08
  31. By: Alain Kabundi; NtuthukoTsokodibane
    Abstract: With the adoption of the in‡ation targeting (IT) regime in 2000, the South African Reserve Bank (SARB) became independent. With the independence of monetary policy comes accountability to the public at large, which in turn leads to transparency in the conduct of monetary policy. The SARB has come a long way in its communication strategy. In 2014 it adds another layer in its communication strategy by announcing explicitly throughout 2014 that monetary policy was on the rising cycle until normalisation is reached. Monetary policy committee (MPC) statements of March and May 2014 refer to normalisation as the return of the policy rate (repo rate) from the historical lowest level of 5 per cent to the normal level in the long run. Like many central banks, the SARB reduced the policy rate from 12 per cent to 5 per cent following the Global Financial Crisis (GFC).
    Keywords: are Monetary Policy, Central Bank Communication, and Nonparametric Change Point.
    JEL: C14 E43 E52 E58 G14
    Date: 2016–08
  32. By: Hao Jin (Wang Yanan Institute for Studies in Economics (WISE) and Department of International Economics and Trade, School of Economics, Xiamen University)
    Abstract: This paper examines the interactions between fiscal policy and the open economy macroeconomic policy trilemma in a small open economy dynamic stochastic general equilibrium model. I show that the trilemma policy regime choices require fiscal accommodation. Otherwise, when future budget fail to stabilize government liabilities, fiscal imbalance generates exchange rate depreciation, regardless of monetary and capital account policy regimes. In this active fiscal policy regime, fiscal and monetary policy interact to determine the magnitude of exchange rate depreciation, while monetary policy and capital controls manage the timing of the depreciation.
    Keywords: Trilemma, Fiscal Policy, Capital Account Policy, Exchange Rate Stability, Monetary Policy
    Date: 2016–08
  33. By: Hetzel, Robert L. (Federal Reserve Bank of Richmond)
    Abstract: Since 2008, the Eurozone has undergone two recessions, which together constitute the "Great Recession." The combination of a decline in output and disinflation as well as a persistent decline in inflation suggests that contractionary monetary policy was one factor. This paper makes two methodological points. First, in analyzing the causes of the Great Recession, it is important to distinguish between credit and monetary policy. Second, a multiplicity of estimated models can "explain" the Great Recession. In practice, economists choose between models through an associated narrative that adds additional information about causation.
    JEL: E52 E58
    Date: 2016–08–23
  34. By: Surico, P.; Trezzi, R.
    Abstract: A major change of the property tax system in 2011 generated significant variation in the amount of housing taxes paid by Italian households. Using new questions added to the Survey on Household Income and Wealth (SHIW), we exploit this variation to provide an unprecedented analysis of the effects of property taxes on consumer spending. A tax on the main dwelling leads to large expenditure cuts among households with mortgage debt and low liquid wealth but generates only small revenues for the government. In contrast, higher tax rates on other residential properties reduce private savings and yield large tax revenues.
    Keywords: Fiscal consolidation, marginal propensity to spend, mortgage debt, residential property taxes
    JEL: E21 E62 H31
    Date: 2016–08–24
  35. By: Ralf R. Meisenzahl
    Abstract: March 24, 2015{{p}}The Federal Reserve's Overnight and Term Reverse Repurchase Agreement Operations in the Financial Accounts of the United States{{p}}{{p}}Ralf R. Meisenzahl1{{p}}{{p}}This note explains how the Federal Reserve's overnight and term reverse repurchase agreement (RRP) operations are reported in the Federal Reserve's Financial Accounts of the United States (formerly known as the Flow of Funds Accounts). Beginning with the March 2015 publication of the Financial Accounts, the securities repurchase transactions of the monetary authority, which include monetary authority repurchase agreements (RPs) and RRPs, will show, as separate line items (in tables F.109, link, and L.109, link), (1) RRP operations that have been conducted as part of the Federal Reserve's Overnight Reverse Repurchase Agreement Operational Exercise since September 2013, combined with term RRP operations that were first conducted in December 2014, and (2) other RPs and RRPs.2 The overnight and term RRP operations will be also appear as memo items on the federal funds and security repurchase agreements instrument tables (F.207, link, and L.207, link). Further details are provided below.{{p}}{{p}}Background on the Federal Reserve's overnight and term RRP operations{{p}}Federal Reserve RPs and RRPs are conducted by the Open Market Trading Desk (the "Desk") at the Federal Reserve Bank of New York. In an RP transaction, the Desk purchases a U.S. government security from an eligible counterparty that agrees to repurchase the same security at a specified price at a specific time in the future. An RRP transaction is the opposite of an RP: The Desk sells a U.S. government security and agrees to repurchase it later. For an overnight RP / RRP, the time between the purchase and repurchase is one business day, while for term RPs / RRPs, the interval can be as many as 65 business days.{{p}}{{p}}The Federal Open Market Committee (FOMC) first authorized the Desk to conduct a series of fixed-rate overnight RRP operations involving U.S. Government securities, including agency securities, in September 2013.3 The FOMC has indicated that it plans to use an overnight RRP facility to help control the federal funds rate during the monetary policy normalization process.4{{p}}{{p}}The overnight RRP operational exercise started on September 23, 2013. Take-up was initially limited to $0.5 billion per counterparty, although this limit was raised to $1 billion a few days later. The limit was subsequently raised in a series of steps, and reached $30 billion per counterparty by late September 2014, when an aggregate limit of $300 billion was also imposed on each overnight RRP operation. The total number of eligible RRP counterparties has increased over the duration of the exercise from 140 in September 2013 to 164 in March 2015.5{{p}}{{p}}In October 2014, the FOMC authorized a series of term RRP operations with an aggregate limit of $300 billion to begin in December 2014 and mature in early January 2015.6 In January 2015, the FOMC authorized the Desk to conduct $200 billion of term RRPs over the March 2015 quarter-end.7 Figure 1 shows combined quarter-end take-up of overnight and term RRP (small amounts of term RRPs that were conducted in testing prior to the fourth quarter of 2014 are not included).{{p}}Figure 1: Quarter-end Take-up in Federal Reserve RRP operations{{p}}Figure 1: Quarter-end Take-up in Federal Reserve RRP operations. See accessible link for data.{{p}}{{p}}{{p}} Source: Federal Reserve Bank of New York:{{p}}{{p}}Accessible version{{p}}{{p}}Reporting overnight and term RRP operations in the Financial Accounts{{p}}Figure 2 shows the effects of an RRP transaction on the monetary authority's and an RRP counterparty's balance sheets. The asset side of the monetary authority's balance sheet is unaffected; it continues to show the securities that have been sold temporarily under the RRP operations. The effect of the RRP is to shift the composition of the monetary authority's liabilities. Its RP ("repo") liabilities expand by $100 (recall that RPs are the opposite of RRPs). At the same time, its reserve liabilities--deposits held by banks and other depository institutions in their accounts at the Federal Reserve--decrease by $100. That is, when a counterparty lends cash to the Federal Reserve through RRPs, the Federal Reserve receives the cash by debiting the reserve account of the bank that clears the counterparty's trade. The RRP operational exercise therefore does not change the overall size of the monetary authority's balance sheet.{{p}}Figure 2: Effects of Reverse Repurchase Agreement on Balance Sheets{{p}}Figure 2: Effects of Reverse Repurchase Agreement on Balance Sheets. See accessible link for data.{{p}}{{p}}Accessible version{{p}}{{p}}Starting with the March 2015 publication of the Financial Accounts of the United States, the balance sheet of the monetary authority (table L.109, link) includes two memo items under securities repurchase agreements. These items are (1) the combined amount of Federal Reserve liabilities in the overnight and term RRP operations as of the last day of each quarter, as shown in figure 1, and (2) Federal Reserve net liabilities under other RPs, which mostly have foreign official and international accounts as counterparties.{{p}}{{p}}Eligible counterparties for Federal Reserve's overnight and term RRP operations include four types of financial institutions: primary dealers, banks (domestic and foreign banking offices in the United States), government-sponsored enterprises (GSE), and money market mutual funds. Figure 3 shows that the dominant type of counterparty for the quarter-end overnight RRP operations has been money market mutual funds.8{{p}}Figure 3: Quarter-end RRP Take-up by Type of Financial Institution{{p}}Figure 3: Quarter-end RRP Take-up by Type of Financial Institution. See accessible link for data.{{p}}{{p}}{{p}} Source: Federal Reserve Bank of New York:{{p}}{{p}}Accessible version{{p}}{{p}}In addition to the monetary authority sector table, The Financial Accounts of the United States also reports volumes of overnight and term RRP operations on the instrument table for Federal Funds and Security Repurchase Agreements (table L.207, link). On this table, the RRP operations appear as a memo item showing the monetary authority's RRP liabilities and money market funds' and other financial institutions' RRP assets.9{{p}}{{p}}References{{p}}Frost, Josh, Lorie Logan, Antoine Martin, Patrick McCabe, Fabio Natalucci, and Julie Remache (2015). "Overnight RRP Operations as a Monetary Policy Tool: Some Design Considerations (PDF)," Finance and Economics Discussion Series 2015-010. Board of Governors of the Federal Reserve System (U.S.).{{p}}{{p}}1. I would like to thank Brian Bonis, Jane Ihrig, Patrick McCabe, John McGowan, Maria Perozek, William Riordan and Paul Smith for helpful comments. Return to text{{p}}{{p}}2. Other RPs and RRPs include those conducted with foreign official and international counterparties. Weekly Federal Reserve's balance sheet data, including all RPs and RRPs, are reported in the Federal Reverse's H.4.1 release ( Return to text{{p}}{{p}}3. The Minutes from the September 2013 FOMC meeting, which report the first authorization of overnight RRP operations, can be found at In December 2014, the FOMC authorized overnight RRP operations until January 29, 2016 ( For a detailed description of the RRP operational exercise, see Frost et al (2015). Return to text{{p}}{{p}}4. See the FOMC's Policy Normalization Principles and Plans, published in September 2014, which can be found at Return to text{{p}}{{p}}5. Lists of currently eligible counterparties is published by the Federal Reserve Bank of New York. The list of primary dealers serving as trading counterparties for the implementation of monetary policy can be found at The expanded list of counterparties that are also eligible for participation in RRP operations can be found at Return to text{{p}}{{p}}6. The FOMC authorization of the term RRP operations is included in the Minutes from the October 2014 FOMC meeting, available at Term RRP amounts can be retrieved from Return to text{{p}}{{p}}7. See Return to text{{p}}{{p}}8. The Federal Reserve Bank of New York publishes the composition of the daily overnight RRP take-up data with a three month delay at Return to text{{p}}{{p}}9. Other include primary dealers, banks (domestic and foreign banking offices in the United States), and government-sponsored enterprises. Return to text{{p}}{{p}} Disclaimer: FEDS Notes are articles in which Board economists offer their own views and present analysis on a range of topics in economics and finance. These articles are shorter and less technically oriented than FEDS Working Papers.{{p}}Search Working Papers{{p}}{{p}}Last update: March 24, 2015
    Date: 2015–03–24
  36. By: Nikola Mirkov; Igor Pozdeev; Paul Söderlind
    Abstract: We ask whether the markets expected the Swiss National Bank (SNB) to discontinue the 1.20 cap on the Swiss franc against the euro in January 2015. In the runup to the SNB announcement, neither options on the euro/Swiss franc nor FX liquidity indicated a significant shift in market expectations. Furthermore, we find that the SNB's verbal interventions during the period of cap enforcement increased the credibility of the cap by reducing the uncertainty of future euro/Swiss franc rate. We conclude that the markets did not anticipate the discontinuation of the policy.
    Keywords: Swiss franc, implied volatilities, market expectations
    JEL: E58 E44 G12
    Date: 2016
  37. By: Chiu, Ching-Wai (Jeremy) (Bank of England); Harris, Richard (University of Exeter); Stoja, Evarist (University of Bristol); Chin, Michael (Bank of England)
    Abstract: In this paper, we investigate the dynamic relationship between financial market volatility, macroeconomic fundamentals and investor sentiment, employing a two-factor model to decompose volatility into a persistent long-run component and a transitory short-run component. Using a structural VAR model with Bayesian sign restrictions, we show that adverse shocks to aggregate demand and supply cause an increase in the persistent component of both stock and bond market volatility, and that adverse shocks to the persistent component of either stock or bond market volatility cause a deterioration in macroeconomic fundamentals. We find no evidence of a relationship between the transitory component of volatility and macroeconomic fundamentals. Instead, we find that the transitory component is more closely associated with changes in investor sentiment. Our results are robust to a wide range of alternative specifications.
    Keywords: Stock and bond market volatility; two-factor volatility model; macroeconomic fundamentals; structural vector autoregression; Bayesian estimation
    JEL: C32 E32 E44
    Date: 2016–08–16
  38. By: Michele Cavallo
    Abstract: November 13, 2014{{p}}The Surprising Strength of U.S. Imports During the Recovery{{p}}{{p}}Michele Cavallo{{p}}{{p}}Import and exports of goods and services, after rebounding sharply in the immediate post-recession period, have more recently returned to a pace of growth more in line with their pre-recession averages (Figure 1). On net, since the start of the recovery in 2009:Q3 the external sector has made a small negative contribution to U.S. growth. This contribution, as indicated in Table 1, is noticeably less negative than the average drag during the previous three recoveries. In part, this difference reflects the larger contribution of exports in this recovery, which have been supported by faster foreign trade-weighted GDP growth and less dollar appreciation than typical during recoveries. The relatively smaller drag from net exports also reflects the fact that total imports have subtracted from growth less than in previous recoveries, an outcome that might have been expected given the lower dollar and the slower pace of U.S. growth in the current recovery. However, rather than reflecting restraint from these variables, the smaller drag of imports on GDP growth has been the result of the recent trend decline in the volume of oil imports, in turn driven by the ongoing expansion in U.S. oil production.{{p}}Figure 1{{p}}Figure 1: Real Import and Export Growth. This figure shows the quarterly annualized growth rates of real imports and exports of goods and services from the first quarter of 2000 to the second quarter of 2014 on the vertical axis. The black line shows the growth rate of real imports, while the red line shows the growth rate of real exports. The vertical axis ranges from negative 50 percentage points to positive 40 percentage points. The vertical light blue bar in the figure marks the second quarter of 2009, the period dated as the end of the Great Recession, thus immediately preceding the start of the current recovery.{{p}}{{p}}Table 1{{p}}Contributions to GDP Growth during Recoveries*, Percentage points Current Previous{{p}}1. Net Exports -0.14 -0.34{{p}}Of which:{{p}}2. Exports 0.76 0.65{{p}}3. Imports -0.89 -1{{p}}Of which:{{p}}4. Oil Imports 0.11 -0.04{{p}}5. Non-oil Imports -1.01 -0.96{{p}} Memo:{{p}}6. GDP Growth 2.21 3.71{{p}}{{p}}*Annual rate; Average of 20 quarters after trough; Recoveries are U.S. GDP expansions following the business cycle troughs of 1982: Q4, 1991:Q1, 2001:Q4, and 2009:Q2 as dated by the NBER.{{p}}{{p}}{{p}}In contrast, non-oil imports have expanded as strongly as in previous recoveries, restraining growth by about the same magnitude. Since the start of the recovery, the ratio of non-oil imports to GDP has risen sharply, rebounding from a stunning decline during the recession to return to its pre-recession high of nearly 15 percent. This rapid rebound partly reflects the highly cyclical nature of the goods portion of imports, which tends to fall more than GDP in recessions and then outpace GDP in recoveries. However, considering the weaker dollar and the more subdued U.S. GDP growth in the current recovery, it is surprising how strong import growth has been.{{p}}{{p}}What might explain the surprising strength in non-oil imports? One possibility is that the recession has led to a permanent shift in demand towards imports. This shift could reflect either a loss in U.S. production capacity during the recession, so that the recovery in demand is now being met by higher imports rather than increased domestic production or through an increased preference for imports. Alternatively, the strength in imports might reflect the particular composition of domestic demand during this recovery, with demand growth more concentrated in categories with larger import shares.{{p}}{{p}}To verify the importance of import substitution versus demand composition effects in boosting imports, I examine evidence from 22 manufacturing sectors. The scatterplot in Figure 2 compares growth in imports for a given manufacturing category over the recovery, on the vertical axis, to growth in industrial production, in that same category, shown on the horizontal axis. The figure shows that, since the recession trough, sectors experiencing larger increases in imports have generally also exhibited larger increases in industrial production. This suggests that the shift up in aggregate imports is not being driven primarily by substitution towards imports, but rather by increases in domestic demand that fall upon both domestic products and imports.{{p}}{{p}}Turning to the role of demand composition in boosting imports, Figure 3 compares the increase in real domestic demand for these same industries (on the horizontal axis) with the extent of import penetration in the industry (on the vertical axis). Import penetration is defined as the share of domestic demand that is supplied by imports and is measured over the two years preceding the recession. The scatterplot reveals that sectors with larger increases in domestic demand since the start of the recovery are also those that historically have had higher import penetration ratios. These data support the hypothesis that the increase in the import-to-GDP ratio in part reflects a recent shift in demand towards categories that are relatively import intensive.{{p}}Figures 2 and 3{{p}}Figure 2 (left): Imports and Industrial Production. This figure shows a scatterplot comparing the growth rates in industrial production for 22 manufacturing sectors from the second quarter of 2009 to the second quarter of 2014, on the horizontal axis, to the growth rates in real imports for the same sectors during the same period, on the vertical axis. The horizontal axis ranges from negative 5 percentage points to positive 20 percentage points. The vertical axis ranges from negative 5 percentage points to positive 25 percentage points. The upward-sloping black solid line displays the corresponding linear fitted trendline. Figure 3 (right): Import Penetration and Domestic Demand. This figure shows a scatterplot comparing the growth rates in real domestic demand for 22 manufacturing sectors from the second quarter of 2009 to the second quarter of 2014, on the horizontal axis, to the averages of the import penetration ratios for the same sectors computed over 2006 and 2007. The horizontal axis ranges from negative 5 percentage points to positive 20 percentage points. The vertical axis ranges from negative 10 percentage points to positive 100 percentage points. The upward-sloping black solid line displays the corresponding linear fitted trendline.{{p}}{{p}}{{p}}{{p}}Figure 4 provides further evidence in support of this view. Personal consumption expenditures (PCE) on goods (the red bar) and equipment investment (the green bar), account for proportionately more imports than do other major spending categories. So far, in the recovery, growth of goods PCE has been positive while growth in equipment investment has been quite strong relative to an aggregate of all other spending categories (the little light blue bar). In contrast, growth in these other categories, including fixed investment in structures and government expenditures, has been generally subdued, unlike in the typical recovery when these sectors were historically more supportive of growth.{{p}}{{p}}The lopsided composition of demand growth might therefore help explain the greater proportion of imports in this recovery. To corroborate the validity of this conjecture, I estimate a forecast model for non-oil goods imports that uses goods PCE, equipment investment, and the change in private inventories as explanatory variables for import demand, and contrast its prediction with that of a baseline model that uses total U.S. GDP as independent variable.1 In comparison to the baseline model, this alternative model thus excludes the components of demand with less import content. The blue line in Figure 5 shows that the ratio of non-oil imports to GDP predicted by the alternative model captures the actual rebound better than the baseline model (the red line), which predicts the ratio to stabilize well below its pre-recession level.{{p}}Figures 4 and 5{{p}}Figure 4 (left): Real Growth. This figure shows a bar chart with the annualized real growth rates for various components of U.S. gross domestic product (GDP) from the second quarter of 2009 to the second quarter of 2014. The blue and the red bars on the left side show the real growth rates for total GDP and personal consumption expenditures (PCE) on goods, respectively. The green and light blue bars on the right side show the real growth rates for fixed investment in equipment and an aggregate of the other major final domestic demand categories, respectively. The other major final domestic demand categories include personal consumption expenditures on services, private fixed nonresidential investment in structures and intellectual property products, private fixed residential investment, and government expenditures. The vertical axis ranges from zero percentage points to positive 12 percentage points. Figure 5 (right): U.S. Imports of Non-Oil Goods and Services. This figure shows the ratio of U.S. non-oil imports goods and services to GDP from the first quarter of 2000 to the second quarter of 2014. The black solid line indicates the ratio as measured in the actual data. The red dashed line displays the ratio as predicted on the basis of a baseline model that uses total U.S. GDP as the explanatory variable for import demand. The blue dash-dotted line depicts the ratio as predicted on the basis of an alternative model that uses personal consumption on goods, equipment investment, and the change in private inventories as explanatory variables for import demand. The vertical axis ranges from positive 10.5 percentage points to positive 15.5 percentage points.{{p}}{{p}}{{p}}{{p}}This finding reinforces the view that the composition of demand growth is key to understanding the unexpected strength of imports in this recovery. In line with this reasoning, as sectors with less import content pick up again going forward, non-oil imports might shift back towards a more traditional alignment with GDP.{{p}}{{p}}{{p}}{{p}}{{p}}1. Both models also control for changes in relative prices via the real exchange rate. Return to text{{p}}{{p}} Disclaimer: IFDP Notes are articles in which Board economists offer their own views and present analysis on a range of topics in economics and finance. These articles are shorter and less technically oriented than IFDP Working Papers.{{p}}Search Working Papers{{p}}{{p}}{{p}}{{p}}Skip Meet Economists Section{{p}}{{p}}Meet the Economists{{p}}All Economists{{p}}By Field of Interest{{p}}Financial Economics{{p}}International Economics{{p}}Macroeconomics{{p}}Mathematical and Quantitative Methods{{p}}Microeconomics{{p}}{{p}}Skip stay connected section{{p}}{{p}}Stay Connected{{p}}Twitter{{p}}YouTube{{p}}RSS Feeds{{p}}Subscribe{{p}}{{p}}{{p}}Last update: November 13, 2014
    Date: 2014–11–13
  39. By: Zarate, Cristina A.
    Abstract: Abstract The monetary trilemma states that it is impossible for a monetary authority to simultaneously have a fixed foreign exchange rate within a context of capital mobility and an independent monetary policy which targets internal objectives. The purpose of this paper is to validate said theory through a model which considers the capital and financial account as a function of the of currency exchange rate, the domestic and international interest rate and the inflation rate. The results show that even if the three objectives are desirable, it is impossible to to meet them simultaneously, in addittion to stating how the Central Bank can overcome this trilemma.
    Keywords: Trilema monetario, Política monetaria, tipo de cambio, tasa de interés
    JEL: E52 E58
    Date: 2016–08–01
  40. By: Juliana Jaramillo-Echeverri.; Adolfo Meisel-Roca.
    Abstract: Entre 1930 y 1951 Colombia enfrentó grandes cambios y diversos choques económicos internos y externos. Este artículo estudia la política monetaria del Banco de la República y las nuevas funciones que adquirió durante esa época, dentro de las que se cuentan la administración de las salinas y las minas de esmeraldas. Por el lado de la contribución cultural se destaca su aporte por medio de la apertura de la biblioteca del Banco y el Museo del Oro. Pese a las crisis que tuvo que afrontar, el balance de la economía colombiana de este periodo fue positivo, sobre todo en el contexto de una desaceleración de la economía mundial y regional. Ese resultado se basó, en gran parte, en el buen desempeño de las exportaciones y el cambio estructural que representó la industrialización por sustitución de importaciones. Classification JEL: E31, E42, E58.
    Keywords: Banco de la República, Colombia, política monetaria.
    Date: 2016–01
  41. By: Hashmat U. Khan (Department of Economics, Carleton University); Jean-François Rouillard (Département d'économique, Université de Sherbrooke)
    Abstract: Why does residential investment lead output in the US and Canada but it is coincident in eurozone countries? In this paper we explore the role of home-equity loans used to boost consumption as a channel that affects residential investment. We consider a multi-agent model where some home-owning households face borrowing constraints that reflect home-equity loans or refinancing constraints. The main contribution of our paper is to highlight that the severity of the households' borrowing constraints in an economy can generate both stylized facts of residential investment dynamics. In US and Canada, a greater proportion of households rely on home-equity loans relative to eurozone countries. This difference matters for the distinct residential investment dynamics observed across countries.
    Keywords: Home-Equity Loans, Borrowing Constraints, Residential Investment, Business Cycles.
    JEL: E22 E32 R21 R31
    Date: 2016–08
  42. By: Aikman, David (Bank of England); Bush, Oliver (London School of Economics); Davis, Alan (University of California)
    Abstract: We have entered a world of conjoined monetary and macroprudential policies. But can they function smoothly in tandem, and with what effects? Since this policy cocktail has not been seen for decades, the empirical evidence is almost non-existent. We can only fix this shortcoming in a historical laboratory. The Radcliffe Report (1959), notoriously sceptical about the efficacy of monetary policy, embodied views which led the United Kingdom to a three-decade experiment of using credit controls alongside conventional changes in the central bank interest rate. These non-price tools are similar to policies now being considered or used by macroprudential policymakers. We describe these tools, document how they were used by the authorities, and craft a new, largely hand-collected dataset to help estimate their effects. We develop a novel identification strategy, which we term Factor-Augmented Local Projection (FALP), to investigate the subtly different impacts of both monetary and macroprudential policies. Monetary policy acted on output and inflation broadly in line with consensus views today, but credit controls had markedly different effects and acted primarily to modulate bank lending.
    Keywords: Monetary policy; macroprudential policy; credit controls
    JEL: E50 G18 N14
    Date: 2016–08–19
  43. By: Hommes, C.H. (University of Amsterdam); Lustenhouwer, J. (University of Amsterdam)
    Abstract: We study monetary policy in a New Keynesian model with heterogeneity in expectations. Agents may choose from a continuum of forecasting rules and adjust their expectations based on relative past performance. The extent to which expectations are anchored to the fundamentals of the economy turns out to be crucial in determining whether the central bank (CB) can stabilize the economy. When expectations are strongly anchored, little is required of the CB for local stability. Only when expectations are unanchored, the Taylor principle becomes a necessary condition. More aggressive policy may however be required to prevent coordination on almost self-fulfilling optimism or pessimism. When the zero lower bound on the nominal interest rate (ZLB) is accounted for, the inflation target must furthermore be high enough, in order to prevent coordination on self-fulfilling liquidity traps and deflationary spirals.
    Date: 2016
  44. By: Christian Miller; Casey Clark
    Abstract: FEDS Notes Print{{p}}May 11, 2016{{p}}Where do I see the Monetary Policy Normalization Tools on the Fed's Balance Sheet and{{p}}Income Statement?{{p}}Christian Miller and Casey Clark1{{p}}On December 16, 2015, the Federal Open Market Committee (FOMC) determined it was appropriate to raise the effective federal funds{{p}}rate from the 0 to 25 basis point range it had been set at since late 2008. This note highlights where some of the key elements of the{{p}}FOMC's approach to policy normalization are reported on the Federal Reserve's website. Specifically, this note focuses on the interest on{{p}}excess reserves (IOER) rate, excess reserve balances, and interest expense on excess reserves. This note also identifies where{{p}}information can be found on the overnight reverse repurchase agreement (ON RRP) offering rate and the associated Federal Reserve{{p}}balances and interest expense.{{p}}Background on Federal Reserve publications{{p}}The Federal Reserve Board's Statistical Release H.4.1,"Factors Affecting Reserve Balances of Depository Institutions and Condition{{p}}Statement of Federal Reserve Banks," is a weekly publication that presents a balance sheet for each Federal Reserve Bank, a{{p}}consolidated balance sheet for all 12 Reserve Banks, an associated statement that lists the factors affecting reserve balances of{{p}}depository institutions, and several other tables presenting information on the assets, liabilities, and commitments of the Federal Reserve{{p}}Banks. Table 1 presents details on the factors that supply and absorb reserve balances, as well as the level of reserve balances--that is,{{p}}funds that depository institutions hold on deposit at the Federal Reserve to satisfy reserve requirements and funds held in excess of{{p}}requirements.2 Table 5 presents the balance sheet of the Federal Reserve System.{{p}}Annually, the Federal Reserve System releases the combined annual financial statements for the Federal Reserve Banks (combined{{p}}statements), as well as statements for the 12 individual Federal Reserve Banks, which provide a significant amount of information about{{p}}the assets, liabilities, earnings, and expenses of the Reserve Banks. The financial statements are audited annually by an independent{{p}}auditing firm. In addition to the annual financial statements, the Federal Reserve System releases quarterly the Federal Reserve Banks{{p}}Combined Quarterly Financial Reports, which provide unaudited quarterly updates to the information presented in the annual financial{{p}}statements.{{p}}IOER and reserve balances{{p}}The FOMC has stated that the IOER rate will be a primary tool during the normalization period.3 Depository institutions should be{{p}}unwilling to lend to any private counterparty at a rate lower than the rate they can earn on balances maintained at the Federal Reserve.{{p}}As a result, an increase in the IOER rate will put upward pressure on a range of short-term interest rates. In effect, raising the IOER rate{{p}}allows the Federal Reserve to increase the value that depository institutions place on reserve balances, which will have market effects{{p}}similar to those associated with a reduction in the quantity of reserves in the traditional, quantity-based mechanism for tightening the{{p}}stance of monetary policy.4 The IOER rate paid on excess reserve balances can be found on the Board of Governors' "Interest on{{p}}Required Balances and Excess Balances" page. Although the Federal Reserve pays interest on required reserves (IORR) in addition to{{p}}IOER, the marginal return of an additional dollar of reserves to a depository institution is the IOER rate given the large amount of excess{{p}}reserves in the System.{{p}}The H.4.1 reports the level of aggregate reserve balances--required and excess reserve balances together--on a weekly basis in Tables 1{{p}}and 5. Table 1 reports "Reserve balances with Federal Reserve Banks," which deducts depository institution overdrafts from overall{{p}}reserve balances.{{p}}Figure 1{{p}} FRB: FEDS Notes: Where do I see the Monetary Policy Normalization Tools on the Fed's... Page 1 of 5{{p}} 5/11/2016{{p}}In Table 5, the line item "Other deposits held by depository institutions" reports both required and excess reserve balances, but is not{{p}}reduced by any overdrafts which would be reported in the assets section of table 5. Thus, this value may not always align with "Reserve{{p}}balances with Federal Reserve Banks" in Table 1.{{p}}The Statistical Release H.3, "Aggregate Reserves of Depository Institutions and the Monetary Base," reports reserve balances maintained{{p}}by month and by two-week maintenance period. "Reserve balances maintained/Total" for a given two-week maintenance period on Table{{p}}1 of the H.3 is the average of the two "Reserve balances with Federal Reserve Banks" weekly averages reported in the given{{p}}maintenance period in Table 1 of the H.4.1.{{p}}Interest expense on reserves is calculated based on balances that comprise "Other deposits held by depository institutions" reported in{{p}}Table 5. Information on how interest is calculated on required and excess reserves can be found in the Reserve Maintenance Manual.{{p}}Interest expense on reserves held by depository institutions is presented in the combined statements and quarterly reports. Within these{{p}}reports, interest expense can be found in the Combined Statements of Income and Comprehensive Income (SOI) under the "Interest{{p}} Expense: Deposits: Depository institutions" line of the statements. The expense information is presented inclusive of required and excess{{p}}reserves and does not provide the interest expense related to excess reserves separately.5{{p}}Figure 2{{p}}Figure 3{{p}} FRB: FEDS Notes: Where do I see the Monetary Policy Normalization Tools on the Fed's... Page 2 of 5{{p}} 5/11/2016{{p}}ON RRP offering rate and RRPs{{p}}Another administered rate to help control the level of the effective federal funds rate is the overnight reverse repurchase program (ON{{p}}RRP) offering rate. In general, any counterparty that is eligible to participate in the ON RRP facility should be unwilling to invest funds{{p}}overnight with another counterparty at a rate below the ON RRP rate. Information on the ON RRP operations can be found on the Federal{{p}}Reserve Bank of New York's "Temporary Open Market Operations" website.{{p}}Both Table 1 and Table 5 of the H.4.1 report "Reverse repurchase agreements" under "Factors absorbing reserve balances" and{{p}}"Liabilities," respectively.{{p}}Figure 4{{p}}Figure 5{{p}} FRB: FEDS Notes: Where do I see the Monetary Policy Normalization Tools on the Fed's... Page 3 of 5{{p}} 5/11/2016{{p}}As shown above, Table 1 breaks out RRPs into two categories: "Foreign official and international accounts" and "Primary Dealers and{{p}}expanded counterparties." All RRPs reported in the latter category, include transactions with primary dealers and other financial{{p}}counterparties who have met eligibility criteria to transact in reverse repurchase agreements that serve as an effective tool for managing{{p}}money market interest rates to help support a floor on those rates.6{{p}}In the combined statements and quarterly reports, interest expense information related to RRPs is available in the "System Open Market{{p}} Account: Securities sold under agreements to repurchase" line of the SOI. More detailed information on RRP balances and expense,{{p}}including the break-out of RRP categories equivalent to Table 1 of the H.4.1, is presented in footnote 5 of the combined statements and{{p}}tables 4 and 13 of the quarterly report.{{p}}1. The authors thank Greg Evans, Jane Ihrig, Elizabeth Klee, and Lawrence Mize for comments. Return to text{{p}}2. Table 1 is not the balance sheet, but it is derived primarily from components of the Federal Reserve's balance sheet. Hence, many of the line items reported{{p}}under "Assets" in Table 5 are also reported under "Factors supplying reserve balances," and this parallelism is also evident among line items reported as{{p}}"Liabilities" in Table 5 and "Factors absorbing reserve balances" in Table 1. Return to text{{p}}3. The Fed also raised the primary credit rate when it began raising short-term interest rates. The primary credit rate is the interest rate at which banks can{{p}}borrow reserves overnight from the Federal Reserve. From early 2010 to late 2015, the primary credit rate was set at 75 basis points or 50 basis points above{{p}}the top of the range for the target federal funds rate. Given that reserves are now superabundant and will remain so for some time, depository institutions will not{{p}}need to borrow from the Federal Reserve and so are unlikely to be influenced by the level of the primary credit rate. Return to text{{p}}Figure 6{{p}} FRB: FEDS Notes: Where do I see the Monetary Policy Normalization Tools on the Fed's... Page 4 of 5{{p}} 5/11/2016{{p}}Last update: May 11, 2016{{p}}Home | Economic Research & Data{{p}}4. For a more detailed discussion surrounding the dynamics at play in the federal funds market, please see Ihrig, Meade, and Weinbach. 2015. "Rewriting{{p}}Monetary Policy 101: What's the Fed's Preferred Post-Crisis Approach to Raising Interest Rates?" Journal of Economic Perspective 29 (4): 177-198. Return to{{p}}text{{p}}5. Currently, IOER is set equal to IORR. Therefore, it is possible to approximate interest expense at a higher frequency by multiplying (IOER/365) by the level of{{p}}balances maintained by depository institutions. Return to text{{p}}6. Participation in the ON RRP operations is open to the Federal Reserve's primary dealers as well as its expanded RRP counterparties, which covers a wide{{p}}range of entities including 2a-7 money market funds, banks, and government-sponsored enterprises (Fannie Mae, Freddie Mac, and Federal Home Loan{{p}}Banks). Return to text{{p}}Please cite this note as: Miller, Christian S. and Casey H. Clark (2016). "Where do I see the Monetary Policy Normalization Tools on the{{p}}Fed's Balance Sheet and Income Statement? ," FEDS Notes. Washington: Board of Governors of the Federal Reserve System, May 11,{{p}}2016,{{p}} Disclaimer: FEDS Notes are articles in which Board economists offer their own views and present analysis on a range of topics in{{p}}economics and finance. These articles are shorter and less technically oriented than FEDS Working Papers.{{p}}Accessibility Contact Us Disclaimer Website Policies FOIA PDF Reader{{p}} FRB: FEDS Notes: Where do I see the Monetary Policy Normalization Tools on the Fed's... Page 5 of 5{{p}} 5/11/2016
    Date: 2016–05–11
  45. By: Jean-Philippe Laforte; John M. Roberts
    Abstract: November 21, 2014{{p}}November 2014 update of the FRB/US model{{p}}{{p}}Jean-Philippe Laforte and John Roberts{{p}}{{p}}Introduction{{p}}This FEDS Note is a companion to the most recent release of the FRB/US model of the U.S. economy on the Board's website. The purpose of this note is twofold. First, it briefly outlines and describes the changes to the structure of the public version of FRB/US since its introduction in the spring of 2014. In addition, it compares the dynamics of the current version to that of the original version in response to key shocks.{{p}}{{p}}Changes to the model{{p}}The most recent update of the public version of the FRB/US model differs from its original release in many significant ways.{{p}}{{p}}Re-estimation of the stochastic equations: We re-estimated all stochastic equations following the release of the NIPA annual revision in August over a sample period that now extends an additional year to 2013q4.{{p}}{{p}}Changes to the specification of the labor market sector: The dynamics of the unemployment rate (LUR) in the original public version of FRB/US were based on an estimated "Okun's law" relationship that directly linked the unemployment rate to the output gap. In the new model, the unemployment rate is related to the levels of civilian employment (LEH) and the labor force (LF) by an identity:{{p}}{{p}}\displaystyle LUR_t = 100(1 - \frac{LEH_t}{LF_t}).{{p}}{{p}}Cyclical movements in the unemployment rate are driven by cyclical movements in the labor force and civilian employment. Civilian employment is in turn related to payroll employment, which, as in the previous version of the model, is related to movements in private output and government spending.1{{p}}{{p}}{{p}}Introduction of an estimate of the real gross domestic product adjusted for measurement errors: The updated version of FRB/US introduces an estimate of real GDP that abstracts from measurement error, which we call XGDO. As in the previous version of the model, the published estimate of real GDP, XGDP, is chain-aggregated from various expenditure categories. XGDO is then inferred by adjusting XGDP for an estimate of measurement error (MEP):{{p}}{{p}}XGDO = XGDP/MEP,{{p}}{{p}}{{p}}The new model also introduces real gross domestic income (XGDI), which is calculated by adjusting XGDO with a second estimate of measurement error (MEI):{{p}}{{p}}XGDI = XGDO × MEI{{p}}{{p}}{{p}}The historical estimates of measurement error are derived from the same model used to estimate the latent supply-side variables of the model; documentation about the supply-side model is available here. For forecasting purposes, measurement error follows a simple time-series process. Finally, we now measure the output gap using XGDO rather than XGDP in the numerator:{{p}}{{p}}\displaystyle XGAP2 = 100 \times log(\frac{XGDO}{XGDPT}){{p}}{{p}}Switch to overall business-sector output: The focus on productivity in the model was moved from the nonfarm business sector to the overall business sector. The implementation of this change involved the substitution of the series representing nonfarm business concepts (identified by the expression 'XNF;' for example, XNFB) with those of total private business (now identified simply by 'X,' as in XB) and the re-estimation or appropriate recalibration of the equations in which series of the business appear.{{p}}{{p}}The re-estimation of the supply-side structure of the model: As with the stochastic equations of the model (see above), the supply-side model that is used to create the latent variables used in FRB/US was re-estimated with a sample that now includes observations from 2013. The structure of the supply-side model was also revised to reflect the switch from nonfarm to total business output. These changes did not have any significant impact on the FRB/US estimates of potential output or the natural rate of unemployment.{{p}}{{p}}Changes to the specification of the price-wage sector: In the updated version, the specification of the FRB/US price equation is:{{p}}{{p}}πt = 0.40πt-1 + 0.60πtz + 0.004πt-1 + 0.92μpt-1{{p}}{{p}}{{p}}where πt is current inflation, πtz is "expected" inflation (ZPICXFE), πt is the long-term inflation expectations (or PTR), and μtp is the price markup gap (see the complete documentation for more details). The key difference between the new and original versions is a reduction of the lag order of the "past inflation" term to one (from four). In addition, the estimation sample now spans the period 1988Q1-2013Q4. The specification of the wage equation has remained the same as in the original version, but its coefficients were re-estimated jointly with those of the new price equation. For both the price and wage equations, the influence of past (price or wage) inflation is now lower than before.{{p}}{{p}}In addition to the changes to the structure of the model noted above, the following obsolete variables were deleted from the model: FNFIN, FNFIRN, UFNFIR, HPRDTP, HPRDTW, UXGN, UYHPNT, HGVPI.{{p}}{{p}}The impulse responses of the model to key shocks{{p}}To illustrate the properties of the FRB/US model, we report the simulated responses of inflation, the output gap, and the federal funds rate to eight key disturbances: shocks to the federal funds rate and term premium; shocks to government spending and to the equity premium; shocks to both the level and growth rate of trend multifactor productivity; and the exchange rate and the price of crude oil. In each case, one-period shocks are applied to specific model equations. The persistence of each shock depends on the properties of the equation in which it appears. Details can be found in the equation documentation in the FRB/US model package.{{p}}{{p}}We show results under the two main expectations mechanisms in the FRB/US model, VAR-based expectations and model-consistent expectations. We assume that monetary policy responds according to an inertial version of the Taylor (1999) rule.2 Computer code with full details on how these simulations were generated is available in the main FRB/US model package.3{{p}}{{p}}The top panels of Figure 1 show the responses of the model to a 100-basis-point shock to the model's inertial Taylor rule. As can be seen, the output responses of the model are little affected by the changes to the model introduced in the November release. The reaction of inflation, however, is less persistent in the new version of the model; this is especially evident for version of the model with the VAR-based expectations. The main reason that inflation is now less persistent is the smaller coefficient on lagged inflation in the new version of the model, as discussed above. As before, the effects of the funds rate shock on output and inflation are considerably smaller in the version of the model with model-consistent expectations. As explained in an earlier note, the differential effects largely reflect differences between the inertial Taylor rule and the estimated funds rate equation that is part of the VAR-based expectations mechanism. The VAR's policy rule has more inertia than the Taylor-type policy rule used for these simulations. As a result, the interest rate increase is anticipated to be more persistent under VAR-based expectations and, because of its effect on real long-term interest rates, which causes the output gap and inflation to decline substantially more in this case.{{p}}Figure 1: Impulse Responses to Policy Shocks{{p}}Figure 1: Impulse Responses to Policy Shocks. See accessible link for data.{{p}}{{p}}Accessible Version{{p}}{{p}}The bottom panels of Figure 1 show the responses of the model to a 100-basis-point shock to the 10-year Treasury term premium.4 As in the previous case, the increases in long-term interest rates depress output and inflation. The federal funds rate declines as the inertial Taylor rule prescribes lower interest rates in response to lower output and inflation. The responses of the economy to the term premium disturbances under MCE and VAR-based expectations are much closer to each other than are the responses to a fed funds rate shock, mostly because the effects of the term-premium shock on longer-term interest rates are very similar in the two versions of the model.{{p}}{{p}}Figure 2 shows the effects of two additional shocks related to aggregate demand. The upper panels shows the responses to a one-quarter shock to the model's equation for federal government spending. The initial shock leads to a persistent increase in government spending owing to the inherent persistence in the model equation for government spending. In both versions of the model, output initially rises by about 1 percent of GDP, in line with the size of the innovation. The higher level of output relative to potential leads to increases in the federal funds rate. These higher interest rates "crowd out" private-sector spending and as a consequence, beyond the initial period, the increase in output is typically smaller than the increase in government spending--that is, the government-spending multiplier is less than one. The effects of the shock differ slightly in the two versions of the model. In particular, after three years, output falls below baseline in the VAR version of the model, whereas it stays close to it in the MCE version.{{p}}{{p}}The bottom panels of Figure 2 display the response to an increase of the equity premium by 100 basis points. The output gap turns negative as higher financing costs faced by firms reduce capital spending and households' spending slows as their equity wealth falls. There is a small decline in inflation that lags somewhat the output gap owing to the model's inflation persistence.{{p}}Figure 2: Impulse Responses to Aggregate Demand Shocks{{p}}Figure 2: Impulse Responses to Aggregate Demand Shocks. See accessible link for data.{{p}}{{p}}Accessible Version{{p}}{{p}}Figure 3 shows the effects of shocks to trend multifactor productivity. The top panels show the effects of an immediate and permanent 1-percent increase in the level of trend MFP. Because spending is inertial in FRB/US, the level of real GDP initially changes very little. As a consequence, the initial jump in the level of potential output implies a drop in the output gap. Inflation also falls, mostly reflecting the reduction in marginal cost associated with the increase in productivity. With both the output gap and inflation depressed, the Taylor rule prescribes a lower federal funds rate. The output gap eventually closes, reflecting both the gradual increase in spending in response to higher permanent income and wealth as well as the stimulative effects of lower interest rates.{{p}}Figure 3: Impulse Responses to MFP Shocks{{p}}Figure 3: Impulse Responses to MFP Shocks. See accessible link for data.{{p}}{{p}}Accessible Version{{p}}{{p}}The bottom panels show the effects of an increase in the trend growth rate of MFP that is initially 1 percentage point and then gradually returns to baseline. In this case, spending initially outstrips potential output, as households and firms anticipate higher incomes and wealth before they materialize. Inflation declines, however, reflecting the direct impact of higher productivity on marginal cost. There are thus competing effects on monetary policy. On balance, the positive output gap dominates and the federal funds rate rises.{{p}}{{p}}Figure 4 shows the consequences of changes in two key foreign factors. The top panels display the response of the economy to an initial increase of 10 percent in the exchange rate. An appreciation of the dollar leads to lower exports and higher imports, and thus represents a drag on aggregate demand as seen in the top-left panel. The output gap eventually widens by about 3/4-percentage point after two years in both versions of the model. Thereafter, the output gap returns to baseline more rapidly in the VAR version of the model. Inflation falls below baseline, reflecting both cheaper imports and the negative slack.{{p}}Figure 4: Impulse Responses to Foreign Factors Shocks{{p}}Figure 4: Impulse Responses to Foreign Factors Shocks. See accessible link for data.{{p}}{{p}}Accessible Version{{p}}{{p}}The bottom panels show the effects of a shock to the model's equation for real oil prices that initially raises the price by 10 dollars per barrel. According to the model equation, oil-price movements are not very persistent. The initial effect is a small decline in output, reflecting the depressing effects of higher oil prices on real household incomes and thus on spending. After about a year, however, the output gap turns positive, reflecting both a rebound in spending as well as persistently adverse effects of higher oil prices on the supply-side of the economy in FRB/US. This also explains why core inflation quickly and persistently move above baseline, as shown in the middle panel. Under MCE expectations, inflation barely falls, as the agents already anticipate the gradual and long-lived deterioration in potential.{{p}}{{p}}{{p}}{{p}}{{p}}1. The discrepancy between household and payroll employment also moves cyclically in the new version of the model. Return to text{{p}}{{p}}2. Specifically, let it denote the nominal federal funds rate, r the steady-state real short-term interest rate, πt the four-quarter rate of inflation, πt the inflation objective, and xt the output gap. The policy rule used in the simulations is it = ρiit-1 + (1 - ρi)(r + πt + φπ(πt - πt) + φxxt) + εt, with an interest smoothing parameter ρi of 0.85, and the values for φπ and φx of 0.5 and 1, respectively. Return to text{{p}}{{p}}3. The program pings.prg included in the FRB/US package computes the impulse responses of the eight shocks discussed in this article. Return to text{{p}}{{p}}4. Consistent with the differences in duration, the simulation includes shocks to the 5-year and 30-year Treasury term premiums of 75 and 30 basis points, respectively. Return to text{{p}}{{p}} Disclaimer: FEDS Notes are articles in which Board economists offer their own views and present analysis on a range of topics in economics and finance. These articles are shorter and less technically oriented than FEDS Working Papers.{{p}}Search Working Papers{{p}}{{p}}Last update: November 21, 2014
    Date: 2014–11–21
  46. By: David M. Byrne; Eugenio P. Pinto
    Abstract: March 26, 2015{{p}}The recent slowdown in high-tech equipment price declines and some implications for business investment and labor productivity{{p}}{{p}}David Byrne and Eugenio Pinto1{{p}}{{p}}Investment in high-tech equipment spurred by rapidly falling prices has accounted for an important share of business investment and labor productivity growth since the 1970s (figures 1 and 2).2 During the present recovery, however, spending on high-tech equipment has been lackluster and a marked step-down in labor productivity growth since 2010 has been partly attributed to this weaker impetus from high-tech capital deepening, that is, the increase in high-tech capital services per worker. Labor productivity rose at an average annual rate of just 1 percent from 2010 to 2014 after rising at an average annual rate of 2 percent over the previous 5-year period; the contribution of high-tech equipment capital deepening slipped from 0.3 percentage point to 0.1 percentage point (figure 2). If sustained, the recent tepid pace of high-tech investment would also portend a subdued outlook for labor productivity going forward.{{p}}Figure 1{{p}}Figure 1: Real private nonresidential fixed investment. See accessible link for data.{{p}}{{p}}{{p}} Note: shaded regions indicate recessions. Calculations based on Bureau of Economic Analysis (BEA) and National Income and Product Accounts (NIPA, also commonly referred to as NIPAs) data.{{p}}{{p}}Accessible version{{p}}Figure 2{{p}}Figure 2: Labor productivity and capital deepening. See accessible link for data.{{p}}{{p}}{{p}} Note: Based on John G. Fernald, "A Quarterly, Utilization-Adjusted Series on Total Factor Productivity." FRBSF Working Paper 2012-19 (updated March 2014). Capital deepening constructed with trend hours growth based on a centered 15-year moving average.{{p}}{{p}}Accessible version{{p}}{{p}}From a measurement perspective, the slowing in business spending on high-tech equipment can almost entirely be attributed to a pronounced slowdown in the price index used to deflate current-dollar high-tech equipment investment in the National Income and Product Accounts (NIPAs) (figure 3). And, an anticipated continuation of tepid declines in high-tech prices seems to be an important factor underlying forecasts of slowing high-tech innovation and diminished potential output growth.3{{p}}Figure 3{{p}}Figure 3: High-tech equipment prices. See accessible link for data.{{p}}{{p}}{{p}} Note: High-tech equipment refers to information processing equipment in the NIPAs. Shaded regions indicate recessions. Calculations based on BEA and NIPA data.{{p}}{{p}}Accessible version{{p}}{{p}}In contrast, we believe this slowdown in the NIPA price deflator may be overstated--and business investment therefore understated--as a result of measurement problems caused by a marked shift in computer investment toward imports and less effective efforts to account for rising product quality reflecting, among other factors, the increasing importance of new products and the nature of the import prices data. For this reason, the deceleration of high-tech equipment price declines over the most recent five-year period should not be used as an indicator of slowing technical advance and prospective labor productivity gains well below 2 percent per year without due consideration of these issues.4{{p}}{{p}}To assess the implications of the recent price trends, we consider an alternative scenario where high-tech equipment prices over the past five years continued to decline at a pace similar to that seen over the previous five years (figure 3). Based on this counterfactual shown by the blue dotted line, we estimate that the measured level of real spending on high-tech equipment would be 17 percent higher at the end of 2014 (worth 0.3 percent on the level of real GDP).5 Due to the sharp increase in import penetration of computer equipment in recent years, faster-falling computer prices would significantly raise real computer imports and only slightly increase exports, assuming a symmetric treatment to investment and net exports. Thus, the overall effect on observed GDP would likely be small, as the additional business investment would be largely offset by lower net exports. Similarly, the recent modest labor productivity gains would also not be revised up appreciably, although a portion of the increases in output-per-hour now attributed to total factor productivity would be shifted to capital deepening.{{p}}{{p}}However, the additional business capital would have implications for the outlook for economic activity as it would improve assessments of the supply-side conditions of the economy. In particular, our counterfactual would imply that greater capital deepening would raise the level of trend productivity and potential output by 1/4 percent at the end of 2014 and, if the gap in price declines was carried forward, would raise potential output growth by between 0.1 and 0.2 percentage points per year after 2014. Historically, output has tended to rise at a pace similar to that of productive capital and, in keeping with this notion, the full impact of high-tech capital deepening on productivity has only appeared after a substantial lag, once complementary investment in software, training and other human capital, and intangible capital such as new business processes have been made.6 Thus, our estimated effects above should likely be considerably amplified over time if investment in complementary activities were to be taken into account.7{{p}}{{p}}The slowdown in high-tech equipment price declines and its sources{{p}}The high-tech equipment price deflators employed in the NIPAs moved down at an average annual rate of 1 percent over the 5-year period ending in 2014Q4, 3 percentage points less rapidly than over the preceding 5-year period ending in 2009Q4 (figure 3).{{p}}{{p}}The underlying trends for the major product components of high-tech equipment differed significantly over this ten-year period: Prices for computers and peripheral equipment swung from falling at an average annual rate of 10.6 percent to falling at a rate of 2.3 percent, while the rates of decline for prices of communication equipment and for other high-tech equipment were little changed (figure 4). For this reason, we focus on computers and peripheral equipment, which accounted for more than 80 percent of the slowing of the decline in high-tech equipment prices.{{p}}Figure 4{{p}}Figure 4: High-tech equipment prices by category, five year average inflation. See accessible link for data.{{p}}{{p}}{{p}} Note: Shaded regions indicate recessions. Calculations based on BEA and NIPA data.{{p}}{{p}}Accessible version{{p}}{{p}}Within computers and peripheral equipment, investment price declines have slowed noticeably since 2009 for all components except capitalized system integration services (figure 5). Most notably, price declines for personal computers (PCs) and for multi-user computers (servers) have slowed markedly and account for almost 90 percent of the slowing in the pace of price declines for computers and peripherals overall.8{{p}}Figure 5{{p}}Figure 5: Computers and peripheral equipment prices, five-year average inflation. See accessible link for data.{{p}}{{p}}{{p}} Note: Shaded regions indicate recessions. Calculations based on BEA and NIPA data.{{p}}{{p}}Accessible version{{p}}{{p}}The flattening of computer prices is largely attributable to a jump in import penetration. Since 2010, the share of computer investment accounted for by domestic producers has been much lower than it was previously (figure 6), a consequence of the shuttering of some large domestic establishments in 2008-11.9 ,10 Because prices for imported computers have historically and recently fallen at a slower pace than the domestic price measures, this shift has depressed the rate of price declines for computers (figure 7). As shown by the dashed black line, by simply weighting the import and domestic price indexes, one can get very close to the NIPA investment price index.11{{p}}Figure 6{{p}}Figure 6: Import penetration of computer equipment investment. See accessible link for data.{{p}}{{p}}{{p}} Note: Computer equipment includes PCs and servers and excludes peripheral equipment. Calculations based on data from the Census Annual Survey of Manufacturers, the Census and BEA U.S. International Trade in Goods and Services, and BEA Input-Output Tables.{{p}}{{p}}Accessible version{{p}}{{p}}Indeed, keeping the import share at its 2008 value would imply a price decline of computer equipment that is about 4 percentage points larger (from 4 percent to 8-1/4 percent, at an annual rate) on average during 2011-13. Slower-falling prices for domestically produced computers have also been a factor, but as a consequence of the rise in import penetration, their influence is minor in recent history (figure 7). Moreover, it seems likely that the composition of domestically-produced computers has changed dramatically since 2009 and that may have led to a marked narrowing in the gap between the domestic and import computer price indexes. If we adjusted the domestic price index to keep the average gap during the past five years at about its level during the previous five years, the weighted computer equipment price index would fall by an additional 4 percentage points (from 8-1/4 percent to 12-1/4 percent) on average during 2011-13.{{p}}Figure 7{{p}}Figure 7: Computer equipment price index in the NIPAs. See accessible link for data.{{p}}{{p}}{{p}} Note: Computer equipment includes PCs and servers and excludes peripheral equipment. Shaded region indicates recession. Calculations based on Bureau of Labor Statistics (BLS) price indexes, BEA data, and weights from figure 6.{{p}}{{p}}Accessible version{{p}}{{p}}Measurement challenges for the computer investment price index{{p}}Below, we explain how the computer price index used in the NIPAs is constructed and we discuss in some detail several measurement-related issues that cast doubt on the plausibility of recent trends in this index.{{p}}{{p}}Construction of the NIPA computer price investment index{{p}}The NIPA computer investment price index is constructed by the Bureau of Economic Analysis (BEA) as a chain-weighted aggregate of Bureau of Labor Statistics (BLS) domestic producer price indexes (PPIs) and BLS import price indexes (IPIs) using relative importance weights derived from commodity flows data. We were able to closely track the computer investment price index by mimicking the BEA methodology and, as expected, the index falls much more slowly in recent years, primarily reflecting the greater influence of the IPI from 2011 forward (figure 7).{{p}}{{p}}Estimation of the BLS price indexes{{p}}The BLS PPIs and IPIs used in the computer investment price index are estimated from prices for a sample of individual items as follows.12 An individual item is a specific computer model sold by a particular reporter with certain terms of sale. Item prices in successive months are matched to form price ratios. Because the linked prices are for identical items, these ratios measure price inflation with quality held constant. When an item exits the sample, the price of an item newly selected for inclusion is linked to the exiting item's price after judgmental adjustment for the difference in quality between the items. The item price ratios are aggregated using a weighted geometric mean formula to create a "matched model" price index for the product category.{{p}}{{p}}When sufficient product feature information is available, hedonic regressions of model prices on technical features of the computers can be used to apportion a price change into contributions from quality changes versus price inflation.13 For example, consider an entering PC model with an additional 2 gigabytes of memory priced $100 higher than the otherwise identical exiting model. If the hedonic regression coefficient for 1 gigabyte of memory is $25, the price of the entering model would be reduced by $50 and the remaining $50 difference would be treated as price inflation. In the case of laptop PCs, desktop PCs, and servers, the adjustment of entering item prices to account for quality is informed by hedonic regressions of model prices on technical features of the computers.14{{p}}{{p}}Potential bias from combining import prices and domestic producer prices{{p}}The approach used by BEA to construct investment price indexes for the NIPAs--aggregation of PPIs and IPIs--has a subtle shortcoming: When investment shifts from domestically-sourced equipment to comparable imported equipment, any associated price decline is not reflected in the investment price index. That is, the investment price index properly reflects the rates of change in domestic and in import prices, but does not reflect the difference in price levels between these markets. In effect, because the domestic production price and the import price are collected by distinct BLS price programs, identical items that are merely sourced differently are treated as distinct products, and the aggregation of PPIs and IPIs treats the entire discount associated with switching to a lower-cost imported computer as a reflection of inferior quality. Recent research has shown that failing to capture the substitution from domestically-produced to imported goods results in understatement of price declines.15{{p}}{{p}}Potential bias from incomplete quality control in the BLS price indexes{{p}}Although laudable, in several respects the BLS efforts to control for quality in their price indexes for domestically-produced and imported computers appear incomplete. These shortcomings make the BLS price measures somewhat unreliable and suggest they may understate the pace of price declines in recent years.16{{p}}{{p}}Inadequate information on product characteristics: Because observations used in the BLS imported computer index are essentially collected from importers rather than producers, they likely contain insufficient information to employ the hedonic adjustment discussed above.17 In such cases, BLS price analysts judgmentally split the price premium for the entering product into a quality component and a pure price difference component.18 In principle, the choice made by the price analyst may overstate or understate the share of a price premium corresponding to quality difference. That being said, in cases where supporting evidence on characteristics is not available, BLS price analysts may tend to attribute the difference between the prices of entering and exiting items to inflation, rather than quality. In the computer market, where model prices fall over time and new models typically enter with relatively high prices due to new features, this may well lead to understatement of price declines.{{p}}Delayed introduction of new products to the indexes: Innovative computing platforms rapidly gain market share by offering the user better performance at a lower relative price. In practice, delayed incorporation of a new product in a price index can lead to understatement of price declines.19 This appears to have been a significant problem for the BLS import price index in recent years. Specifically, tablet PCs, which penetrated the computer market rapidly in 2010, were not introduced in the import price index until August, 2012, when they had already obtained noteworthy market share.20{{p}}Products that may need further attention: Products other than laptop PCs, desktop PCs and servers may require more careful effort to control for quality. Most notably, the BLS does not estimate a hedonic model for tablet PCs. This limits the options available to price analysts attempting to control for quality in this increasingly important product.{{p}}Indirect control for performance in BLS hedonic models: The hedonic models used by the BLS to account for quality in PCs and servers employ a set of variables corresponding to technical features of the equipment, such as the amount of memory and the speed of the processor, rather than direct measures of the performance of the equipment on tasks of interest to the user.21 Recent work suggests that, particularly in the past five years, direct control for performance, rather than an input-based hedonic specification, produces markedly faster price declines for microprocessors (MPUs).22 Because MPUs are a central component of PCs and servers, this suggests that the same approach may be needed for those platforms as well.{{p}}{{p}}Potential bias from transfer prices in the import price index{{p}}The BLS import price index is composed of both market prices and transfer prices, that is, prices from exchanges between affiliated parties (e.g. subsidiaries in a multinational company). Transfer prices accounted for 61 percent of the value of computer and electronic products imports included in the IPI for computers in 2013.23 These prices may be affected by tax avoidance and other corporate strategies to minimize costs.24 However, though the transfer price level may be expected to differ from a hypothetical price established in market competition, it isn't immediately apparent that a bias to the growth rate would be introduced by the inclusion of transfer prices. Nevertheless, it is important to bear in mind that the IPI is to a significant extent a measure of the prices used for internal accounting of inventory investment by computer companies, rather than the market price paid by businesses investing in capital.{{p}}{{p}}1. We thank Christina Hovland at the Bureau of Economic Analysis, and Steve Sawyer and Phil Strum at the Bureau of Labor Statistics for providing helpful information. We also thank Tyler Hanson for extensive research assistance. Return to text{{p}}{{p}}2. High-tech equipment corresponds to the "information processing equipment" category in the NIPAs and consists of computers and related equipment, communication equipment, electro-medical equipment, instruments, photocopy and related equipment, and office and accounting equipment. Return to text{{p}}{{p}}3. See Mellman and Feroli, "U.S. capital spending: high-tech is low growth," JP Morgan, February 27, 2014; Gordon (2014), "The Turtle's progress: Secular stagnation meets the headwinds," in Teulings and Baldwin, eds. (2014), Secular Stagnation: Facts, Causes and Cures,; and Fernald (2015), "The Recent Rise and Fall of Rapid Productivity Growth." FRBSF Economic Letter, February 9. Return to text{{p}}{{p}}4. Recent advances in information technology (IT) cannot be assessed by examining high-tech equipment alone, as software currently accounts for a similar share of IT spending. However, only one-fourth of software investment (prepackaged software) features quality-adjusted prices in the NIPAs: after slowing considerably in the early 2000s, price declines of prepackaged software have not decelerated further over the past 5 years (not shown). Return to text{{p}}{{p}}5. This would bring the average annual percent change in real high-tech equipment investment over the current recovery from 5-1/4 percent to 8-1/2 percent, just a bit below the average gains seen in the previous expansion (10 percent). Return to text{{p}}{{p}}6. See Brynjolffson, Hitt, and Yang (2002), "Intangible assets: Computers and organizational capital," Brookings Papers on Economic Activity. Return to text{{p}}{{p}}7. We estimate that the high-tech equipment real capital stock would be 7 percent higher by the end of 2014 and that the high-tech equipment share of capital income would gradually rise, essentially reflecting a boost to the user cost of capital from faster price declines. Accordingly, the level of total real capital services would be 3/4 percent higher at the end of 2014, and would rise 1/4 percentage point per year faster after 2014. Return to text{{p}}{{p}}8. Net sales of used computers also accelerates dramatically. This portion of investment is deflated by a composite index built from, among other pieces, the same price indexes as PCs and servers. Return to text{{p}}{{p}}9. After a wave of offshoring by other companies in the wake of the 2001 downturn, Dell Inc. accounted for the lion's share of U.S. domestic PC production. The company closed plants in Austin, Texas (2008), Lebanon, Tennessee (2009), and Winston-Salem, North Carolina (2010), and shifted production for the U.S. market to Mexico and other offshore locations. A Dell plant in Florida continues to produce highly-specialized PCs. Return to text{{p}}{{p}}10. The import share of storage devices and other computer peripheral equipment have remained little changed since 2008, at about one-half and two-thirds respectively. Return to text{{p}}{{p}}11. We used detailed domestic producer price indexes (PPIs) and import price indexes (IPIs) to create a domestic price index (dashed red line) and an import price index (dotted blue line) for computer equipment. Using the import share in figure 6, we were then able to create a measure of the computer investment price index (dash-dotted black line) which closely tracks BEA's estimate (solid black line). Return to text{{p}}{{p}}12. The same methodology is used for peripheral equipment, except that hedonic indexes are not estimated for use in price adjustment. Return to text{{p}}{{p}}13. Indeed, quality difference could more than account for a higher price for an entering item, in which case the quality-adjusted item price would decline even if the observed item price had increased. Return to text{{p}}{{p}}14. Regressions are performed quarterly with a supplemental dataset. For a description of BLS PPI hedonic adjustment methodology, see Holdway (2001), "Hedonic Models in the Producer Price Index," The same method is employed for import prices. Return to text{{p}}{{p}}15. See Houseman, Kurz, Lengermann, and Mandel (2011), "Offshoring Bias in U.S. Manufacturing," Journal of Economic Perspectives. Return to text{{p}}{{p}}16. While the bias introduced by these issues is not known, it is worth noting that the "Boskin Commission" found that incomplete quality adjustment and delayed introduction of new products introduced a substantial upward bias to the CPI. See Gordon (2006), "The Boskin Commission Report: A Retrospective One Decade Later," International Productivity Monitor. Return to text{{p}}{{p}}17. The sample of U.S. importers is derived from Custom and Board Patrol consumption entry documents and the detailed products are selected after an interview with a BLS economist. In general, the majority of detailed products cannot be consistently priced over time as nearly 40 percent of monthly observations contain price data for newly introduced products. The proportion of new products is almost certainly much higher for computer equipment and importers probably lack enough knowledge about the detailed specifications of the new products they are importing. Return to text{{p}}{{p}}18. Three options are commonly employed by BLS: (1) link the entering item directly to the exiting item--attributing all of the price difference to inflation, (2) link the first change (the "price relative") in the new item price to the last change in the old item price--attributing all of the price difference to quality, and (3);linking to the "cell relative price", a technique that assumes that price inflation for the entering item equals the average across all observed items in the previous period. Return to text{{p}}{{p}}19. This was dramatically the case for the CPI when cell phones were only introduced to the index long after they had become popular. See Hausman (1999), "Cellular Telephone, New Products, and the CPI," Journal of Business & Economic Statistics. However, if prices of incumbent products adjust quickly to equate quality-adjusted prices to the entering product, the price index may properly reflect the influence of the omitted product. Return to text{{p}}{{p}}20. JP Morgan estimates that the size of the U.S. tablet market was $20 billion for 2012 and accounted for 31 percent of PC sales. See "2014 Tablet Model Update," November 7, 2014. Return to text{{p}}{{p}}21. Whether a particular hedonic regression without controls for directly measured performance can be used to adequately control for quality is an empirical question. See Pakes (2002), "A Reconsideration of Hedonic Price Indices with an Application to PCs," NBER Working Paper No. 8715. Return to text{{p}}{{p}}22. See Byrne, Oliner, and Sichel (2014), "How Fast are Semiconductor Prices Falling?" AEI Economic Policy Working Paper Series. Return to text{{p}}{{p}}23. Related-party trade accounted for 50 percent of the value of imported goods in 2013. See "U.S. Goods Trade: Imports & Exports by Related-Parties 2013 (PDF)," May 6, 2014. Return to text{{p}}{{p}}24. For discussion of transfer prices, see Clausing (2000), "The Behavior of Intrafirm Trade Prices in U.S. International Price Data," Return to text{{p}}{{p}} Disclaimer: FEDS Notes are articles in which Board economists offer their own views and present analysis on a range of topics in economics and finance. These articles are shorter and less technically oriented than FEDS Working Papers.{{p}}Search Working Papers{{p}}{{p}}Last update: March 26, 2015
    Date: 2015–03–26
  47. By: Christopher J. Kurz; Norman J. Morin
    Abstract: Print{{p}}Figure 1: Real manufacturing capital stock in equipment and structures{{p}}(Chain weighted, billions $2009){{p}}Accessible version{{p}}Figure 2: Real manufacturing capital stock in equipment and structures{{p}}(Percent change){{p}}March 2, 2016{{p}}Annual Data on Investment and Capital Stocks{{p}}Christopher Kurz and Norman Morin{{p}}As part of the estimation of capacity for publication of its G.17 statistical release on industrial production and capacity utilization, the{{p}}Federal Reserve Board produces annual information on the real capital stock and real investment for detailed industries within the{{p}}manufacturing sector. This note provides a brief overview of the creation of the data, outlines the uses for the data, and is accompanied{{p}}by a public release of the most recent vintage of the capital stock and investment series.{{p}}Investment and Capital Stock Series{{p}}An important component of the estimation of capacity indexes for most manufacturing industries is a measure of the flow of services{{p}}derived from an industry's net stocks of physical assets.1,2 These service flows, called either capital input or capital services, are derived{{p}}from estimates of the real capital stock.{{p}}For each industry, asset-level real capital stocks are estimated using investment data back to the 1860s (for structures) or 1870s (for{{p}}equipment). The capital stocks are constructed using the perpetual inventory method, combining industry-level investment data{{p}}(primarily from the Census Bureau's Annual Survey of Manufactures and Census of Manufactures), asset-level investment data (from{{p}}the fixed assets tables issued by the Bureau of Economic Analysis [BEA]), and asset-level deflators (BEA).3{{p}}Industry-level capital stock measures are produced by chain-weighting the asset-level capital stocks for that industry. Industry-level{{p}}capital stock data begin in 1952, extend through 2013, and cover 86 detailed four-digit manufacturing NAICS industries as well as{{p}}related aggregates.{{p}}A Slowdown in Capital Growth{{p}}Economic growth since the 2007-09 recession has been somewhat slower, on average, than in previous recovery periods because of{{p}}both disappointing productivity growth and investment that has been cyclically weak.4 For the overall economy, nonresidential private{{p}}fixed investment increased at an average annual rate of only about 4-1/4 percent from 2012 through 2015, an unusually slow pace{{p}}during an expansion.5{{p}}Figures 1{{p}}and 2 plot{{p}}the real{{p}}capital stock{{p}}in{{p}}equipment{{p}}and{{p}}structures{{p}}for the{{p}} FRB: FEDS Notes: Annual Data on Investment and Capital Stocks{{p}}1 of 3 3/2/2016 8:43 AM{{p}}Accessible version{{p}}Figure 3: Real manufacturing investment in equipment and structures{{p}}(Chain weighted, billions $2009){{p}}Accessible version{{p}}Figure 4: Real manufacturing investment in equipment and structures{{p}}(Percent change){{p}}manufacturing sector and its rate of change (gross investment less depreciation as a share of the stock at the end of the previous year).{{p}}One striking aspect of the figure is the flattening out of the capital stock since 2000. After increasing around 3 percent per year, on{{p}}average, through the 1960s and 1970s, the growth of the manufacturing capital stock remained positive but slowed dramatically during{{p}}the dual recessions in the early 1980s. The capital stock expanded strongly in the 1990s and then posted unprecedented declines from{{p}}2002 to 2004 and from 2009 to 2011. It has risen only anemically since 2011. The post-2000 contour of the capital stock is a result of the{{p}}pattern of investment spending by manufacturers. As can be seen in figures 3 and 4, since 2000, the cyclical declines in investment{{p}}spending have been of longer duration, and they were not subsequently followed by prolonged bouts of sustained above-average rates{{p}}of investment spending. In 2013, as a result, the level of the real capital stock in equipment and structures for manufacturing remained{{p}}about 2-1/2 percent below its peak in 2001 and real investment in equipment and structures was nearly 5 percent below its peak in{{p}}2000.{{p}}Future Releases of the Investment and Capital Stock Data{{p}}In the coming years, the Federal Reserve Board plans to publish these data on an annual basis, timed to follow releases of the Annual{{p}}Survey of Manufactures and Census of Manufactures, the primary sources for the industry-level investment series used to estimate{{p}}capital stocks. In addition, upcoming releases will include an expanded set of data series. In particular, we plan to publish measures of{{p}}capital input, as the services flow is the primary goal of our investment/capital estimation for the construction of our measures of{{p}}manufacturing capacity. In addition, we plan to provide series for investment and capital for software in manufacturing industries.{{p}}Moreover, the capital input series will be accompanied by a detailed explanation of their method of construction.{{p}}References{{p}}Becker,{{p}}Randy,{{p}}Wayne{{p}}Gray, and{{p}}Jordan{{p}}Marvakov{{p}}(2013),{{p}}"NBER-CES{{p}} FRB: FEDS Notes: Annual Data on Investment and Capital Stocks{{p}}2 of 3 3/2/2016 8:43 AM{{p}}Accessible version{{p}}Accessibility Contact Us Disclaimer Website Policies FOIA PDF Reader{{p}}Manufacturing Industry Database: Technical Notes," Working Paper.{{p}}Gilbert, Charles and Mike Mohr (1996), "Capital Stock Estimates for Manufacturing Industries: Methods and Data," Working Paper.{{p}}Gilbert, Morin, and Raddock (2000), "Industrial Production and Capacity Utilization: Recent Developments and the 1999 Revision{{p}}(PDF)," Volume 86, March, pp. 194–97.{{p}}Fernald, John (2015), "Productivity and Potential Output Before, During, and After the Great Recession," NBER Macroeconomics{{p}}Annual 2014, Volume 29, pp. 1–51, Parker and Woodford. 2015{{p}}Pinto, Eugenio P., and Stacey Tevlin (2014), "Perspectives on the Recent Weakness in Investment," FEDS Notes 2014-05-21. Board of{{p}}Governors of the Federal Reserve System (U.S.).{{p}}1. See Gilbert, Morin, and Raddock, "Industrial Production and Capacity Utilization: Recent Developments and the 1999 Revision (PDF)," Volume 86, March{{p}}2000, pp. 194–97. Return to text{{p}}2. The NBER-CES Manufacturing Industry Database employs the Federal Reserve Board's annual data on Investment and Capital Stocks to derive{{p}}information on depreciation rates and for investment deflators. See Becker, Gray, and Marvakov (2013) for more information. Return to text{{p}}3. Please see the March 1996 document by Gilbert and Mohr "Capital Stock Estimates for Manufacturing Industries: Methods and Data (PDF)" for a detailed{{p}}treatment of how industry-level investment and asset-level investment are combined to form estimates of industry investment across asset categories, and how{{p}}the time series of industry-by-asset investment are used to form estimates of capital stocks.Return to text{{p}}4. See Fernald (2015). Return to text{{p}}5. See Pinto and Tevlin (2014). Return to text{{p}}Please cite this note as:{{p}}Kurz, Christopher J., and Norman J. Morin (2016). "Annual data on Investment and Capital Stocks," FEDS Notes. Washington: Board of{{p}}Governors of the Federal Reserve System, March 2, 2016,{{p}} Disclaimer: FEDS Notes are articles in which Board economists offer their own views and present analysis on a range of topics in{{p}}economics and finance. These articles are shorter and less technically oriented than FEDS Working Papers.{{p}}Last update: March 2, 2016{{p}}Home | Economic Research & Data{{p}} FRB: FEDS Notes: Annual Data on Investment and Capital Stocks{{p}}3 of 3 3/2/2016 8:43 AM
    Date: 2016–03–02
  48. By: Alejandro Perez-Segura; Robert J. Vigfusson
    Abstract: Print{{p}}April 6, 2016{{p}}The relationship between oil prices and inflation compensation{{p}}Alejandro Perez-Segura and Robert J Vigfusson 1{{p}}Between June 2014 and February 2016, oil prices declined from $115 per barrel to near $30 per barrel (the purple line in Figure 1). Over{{p}}the same period, long-run inflation compensation, often used as a proxy for long-run inflation expectations, narrowed from 2.45 percent{{p}}to 1.38 percent (the black line in Figure 1). Long-run inflation compensation is measured by the difference (or spread) between the yields{{p}}for standard ten to five year-ahead Treasury bonds less the same spread between the yields on ten to five year Treasury inflation-protected{{p}}securities (aka TIPS). (See Gürkaynak, Sack, and Wright, 2008 for details.) Long-run inflation compensation reflects not only{{p}}market participants' expectations for future inflation, but also investors' risk aversion to losses resulting from inflation and also different{{p}}market conditions for TIPS relative to that for nominal Treasuries.{{p}}Figure 1: The Puzzling Common Decline in Inflation Compensation and Oil Prices{{p}} Note: Inflation compensation calculated using 10 year and 5 year nominal and inflation adjusted U.S. Treasury securities. Oil prices are measured using the{{p}}front-month futures contract for Brent oil.{{p}}Accessible version{{p}}Noting the common decline in oil prices and long-run inflation compensation, many people have claimed that lower oil prices have{{p}}contributed to the decline in inflation compensation. Although lower oil prices today should result in lower inflation over the next year, a{{p}}current decline in oil prices should not reduce inflation five to ten years in the future.2 If inflation compensation is declining because of{{p}}changes in inflation expectations, the claim that lower oil prices today reduce inflation compensation for a horizon of five to ten years{{p}}ahead is puzzling. To resolve this puzzle, some have proposed that both oil prices and inflation compensation are responding to a{{p}}common factor, such as a deterioration in the outlook for global economic activity.3{{p}}Although the theory that oil price movements and inflation compensation are both responding to a common factor is appealing, to-date{{p}}analysts have provided very little empirical evidence about its plausibility. In this note, we provide new empirical evidence supporting this{{p}}conjecture that changes in the outlook for global economic activity explain the co-movement between oil prices and inflation{{p}}compensation. In particular, we present an empirical strategy to identify changes in oil prices that are a response to economic activity{{p}}(demand-induced) and changes in oil prices that are responding to oil-specific developments (supply-induced). Our main finding is that{{p}}demand-induced oil price declines can explain most of the move in inflation compensation. As a consequence, economists would do well{{p}}to pay less attention to oil prices and more attention to macroeconomic factors in explaining the decline in inflation compensation.{{p}}Identifying Demand-induced and Supply-induced Oil Price Changes{{p}}To better determine the underlying economic factors that may be driving the link between oil prices and inflation compensation, we{{p}}classify daily oil price changes into "supply-induced" and "demand-induced" movements. We base our classification on a simple{{p}}identifying assumption that links movements in oil prices with movements in equity and metals prices. Our assumption is that increases{{p}}in oil supply (either actual or prospective) should cause oil prices to decline. However, since these lower oil prices should improve overall{{p}}economic prospects, equity and metals prices should increase4 . Conversely, worsening economic prospects for global demand should{{p}}cause all three prices to decline. Based on these assumptions, a daily change in oil prices is defined as "demand-induced" if it has the{{p}} FRB: IFDP Notes: The relationship between oil prices and inflation com...{{p}}1 of 4 4/7/2016 7:43 AM{{p}}same sign as the contemporaneous changes in both equity and metals prices. All other price changes are defined as "supply-induced".5{{p}}The decomposition of oil into demand-induced and supply-induced changes is reported below in Figure 2. The purple line in the figure is{{p}}the actual price of oil (the Brent front-month futures price). The green line is how oil prices would have evolved over time if subject to{{p}}only supply-induced changes, while the orange line shows how oil prices would have evolved if subject to only demand-induced{{p}}changes. The results match contemporaneous reports. The sharp increases in oil prices in early 2011 and 2012 related to events in{{p}}Libya and Iran are clearly identified as supply-induced changes. In addition, much of the sharp decline that occurred during the summer{{p}}and early fall of 2014 is identified as a supply-induced change. For much of 2015 and into early 2016, demand induced changes have{{p}}played a much more important role, which is in line with press reports.{{p}}Figure 2: Demand-Induced and Supply-Induced Oil Price Changes Since January 2011{{p}}Accessible version{{p}}Empirical Evidence{{p}}To establish the relationship between changes in inflation compensation and oil prices, we start with a simple daily regression. We report{{p}}results for inflation compensation (measured as the difference between the nominal and real 5-year yields, 5-years ahead). As the{{p}}explanatory variable, we use the contemporaneous change in log oil prices (measured as front-month futures quotes for Brent oil) from{{p}}January 2011 through June 2014. The benefit of ending the sample in June 2014 is that developments in inflation compensation since{{p}}then are explored as an out-of-sample exercise. In this regression, a one percent move in oil prices causes inflation compensation to{{p}}move in the same direction by about 0.6 basis points (HAC robust T-statistics are reported below the coefficients).{{p}}A response of 0.6 basis points (i.e. just over 5/1000 of a percent) may sound miniscule. However, because Brent oil prices have declined{{p}}from $115 per barrel to around $30, the cumulative log change in oil price is large, thus this regression model would attribute 80 of the{{p}}observed 107 basis point decline in inflation compensation to oil price changes.{{p}}Our next regression tests whether inflation compensation responds differently to demand- versus supply-induced oil price changes. In{{p}}particular, we create a dummy variable D, which is equal to one on days when the oil price change is demand-induced (i.e. both metals{{p}}prices, measured using the GSCI metals index, and equity prices, measured using the S&P 500 index, moved in the same direction as{{p}}oil prices). The resulting estimated equation is{{p}}On average, when an oil price decline is associated with both lower metals and equity prices, inflation compensation moves in the same{{p}}direction by almost 1.1 basis points for every log-percentage point change. On all other days, inflation compensation moves in the{{p}}opposite direction of the oil price change. Although this negative coefficient is not statistically significant, the sign is consistent with{{p}}theory that supply-induced increases prices are a negative drag for economic activity, which could reduce far-dated inflation{{p}}expectations. Using a Wald Test, we reject the hypothesis that the coefficients on demand-induced and supply-induced changes are{{p}}equal. The results are consistent with an interpretation that inflation compensation changes are not affected by oil price changes directly,{{p}}but rather by revisions to perceptions of economic growth.{{p}}Although this decomposition is intuitive, there remains a slight puzzle. Although our model explains 65 of the 107 basis point decline in{{p}}inflation compensation from June 2014 to now, it did not do well in the subinterval in the second half of 2014. Between July and{{p}}December 2014, inflation compensation declined 44 basis points, but our model would have predicted only a 7 basis point decline. The{{p}}model's inability to match the decline in inflation compensation from mid-2014 results from the model attributing the sharp decline in oil{{p}}prices in 2014 to being mostly supply-driven, which in our specification does not reduce inflation compensation. Even though we missed{{p}} FRB: IFDP Notes: The relationship between oil prices and inflation com...{{p}}2 of 4 4/7/2016 7:43 AM{{p}}over the subinterval, overall our model does well because inflation compensation moved higher in early 2015 and demand-induced{{p}}changes played a greater role in moving oil prices in the 2015.{{p}}It is possible that what we call demand-induced changes may reflect changes in investors' risk aversion rather than changes in the{{p}}economy's risk prospects. As such, to better approximate changes in risk sentiment, we augmented the model with a measure of{{p}}financial market volatility (the VIX) and also the exchange value of the dollar. The new regression is similar to the original regression, but{{p}}now both the VIX and the dollar also contribute to fitting changes in inflation compensation.{{p}}With the additional variables, our new model explains 76 of the 107 basis point decline in inflation compensation between June 2014{{p}}and February 2016. As indicated by the smaller coefficient on demand-induced oil price changes, some of the decline in inflation{{p}}compensation that we attributed to demand-induced changes has instead been attributed to either the VIX or the dollar. The new model{{p}}better explains what happened in the second half of 2014, predicting 26 of the 44 basis point decline between July and December 2014.{{p}}In addition, as shown in Figure 3, the model (green line) and the data (black line) meet in the spring of 2015 and track closely for the{{p}}remainder of 2015. Only since the start of 2016 have the model and the data again seem to diverge as inflation compensation moved{{p}}lower.{{p}}Figure 3: Our Model Matches Much of the Observed Decline in Inflation Compensation{{p}}Accessible version{{p}}Conclusions.{{p}}Our empirical results suggests that only demand-induced oil price declines cause large declines in inflation compensation. One question{{p}}that still remains is why high frequency changes in assessments of growth should impact the outlook for economic performance{{p}}sufficiently to move inflation expectations five to ten years out. Elliot, Jackson, Raczko, and Roberts-Sklar (2015) hypothesize that one{{p}}reason may be an expectation by market participants that central banks will be constrained by the zero lower bound. If so, this suggests{{p}}that as we move away from the current era of the zero lower bound, the responsiveness of inflation compensation to oil price changes{{p}}may decline.{{p}}Another implication of our work is that if the decline in oil compensation since June 2014 has been demand-driven, then a tightening of{{p}}supply conditions that pushes up the price of oil could further depress inflation compensation. Although the estimated coefficient on{{p}}supply-induced changes is not statistically significant, the coefficient is negative. By operating through standard macroeconomic{{p}}channels, higher oil prices resulting from reduced oil supply could be a drag on economic activity both now and in the future, reducing{{p}}inflation compensation even more.{{p}}References{{p}}Blanchard, Olivier (2016, January 17). The Price of Oil, China, and Stock Market Herding, Retrieved from{{p}}/?p=5341{{p}}Elliott, David, Chris Jackson, Marek Raczko and Matt Roberts-Sklar (2015, October 20). Does oil drive financial market measures of{{p}}inflation expectations? Retrieved from{{p}}Groen, Jan, Kevin McNeil, and Menno Middeldorp (2013, March 25). A New Approach for Identifying Demand and Supply Shocks in the{{p}}Oil Market, Retrieved from{{p}}in-the-oil-market.html{{p}}Gürkaynak, Refet S., Brian Sack, and Jonathan H. Wright, 2008, "The TIPS Yield Curve and Inflation Compensation" FEDS working{{p}} FRB: IFDP Notes: The relationship between oil prices and inflation com...{{p}}3 of 4 4/7/2016 7:43 AM{{p}}Accessibility Contact Us Disclaimer Website Policies FOIA PDF Reader{{p}}paper 2008-05 Retrieved from{{p}}Hamilton, J. D. (2003). "What is an Oil Shock?" Journal of Econometrics, 113, 363–398.{{p}}Kilian, Lutz. (2009). "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market." American{{p}}Economic Review, 99(3): 1053-69.{{p}}Kocherlakota, Narayana (2016, January 14). Information in Inflation Breakevens about Fed Credibility, Retrieved from{{p}}{{p}}Sussman, Nathan and Osnat Zohar (2015, September 16). Oil prices, inflation expectations, and monetary policy, Retrieved from{{p}}{{p}}1. The views in this paper are solely the responsibility of the authors and should not be interpreted as reflecting the views of the Federal Reserve System.{{p}}Return to text{{p}}2. Movements in the slope of the futures curve since June 2014 actually suggest that oil prices might be expected to boost inflation in the coming years.{{p}}Between June 2014 and now, the futures curve for oil delivered five to ten years in the future went from downward sloping (predicting falling oil prices and{{p}}hence a drag on inflation) to upward sloping (predicting rising oil prices and hence a boost to inflation). Return to text{{p}}3. A Bank of England report by Elliot, Jackson, Raczko, and Roberts-Sklar (2015) reviews the issues. Likewise, Kocherlakota (2016) describes how moves in{{p}}inflation compensation (or as he refers to them inflation breakevens) can reflect either changes in inflation forecasts or inflation risk premium. Return to text{{p}}4. This identification is inspired by a long line of academic papers, including Hamilton (2003) and Kilian (2009), that find that oil price increases (resulting either{{p}}from supply or precautionary-demand shocks) are bad for economic growth. In recent weeks, some have claimed that supply-driven oil price changes are{{p}}causing equity prices to move in the same direction, which would be counter to our identification strategy. However, this claim is contentious (see Blanchard{{p}}2016) and, even if this relationship were to have changed in 2016, it would not have affected the empirical estimation whose sample period ends in June 2014.{{p}}Return to text{{p}}5. In terms of related work that looks at demand and supply changes to oil prices, we would like to highlight two related papers. Groen, McNeil, and Middeldorp{{p}}(2013) also use financial markets to distinguish between supply and demand changes to oil prices. However, their method for using financial market{{p}}information is different and, to our knowledge, their decomposition has not been applied to inflation compensation. Sussman and Zohar (2015) also attempt to{{p}}distinguish between supply and demand changes to oil prices by identifying common factors of price changes in oil, metals, and agricultural commodities.{{p}}However, their empirical results find that demand and supply shocks have similar effects on U.S. inflation compensation. In contrast, our identification finds a{{p}}large difference between the effects of demand-induced and supply-induced oil price changes on inflation compensation. It would seem likely that their finding{{p}}results from their use of monthly data, whereas we used daily data, which should provide a finer discrimination between demand and supply effects. Return to{{p}}text{{p}} Disclaimer: IFDP Notes are articles in which Board economists offer their own views and present analysis on a range of topics in{{p}}economics and finance. These articles are shorter and less technically oriented than IFDP Working Papers.{{p}}Last update: April 6, 2016{{p}}Home | Economic Research & Data{{p}} FRB: IFDP Notes: The relationship between oil prices and inflation com...{{p}}4 of 4 4/7/2016 7:43 AM
    Date: 2016–04–06
  49. By: Bengui, Julien (Université de Montréal); Bianchi, Javier (Federal Reserve Bank of Minneapolis); Coulibaly, Louphou (Université de Montréal)
    Abstract: In this paper, we study the optimal design of financial safety nets under limited private credit. We ask when it is optimal to restrict ex ante the set of investors that can receive public liquidity support ex post. When the government can commit, the optimal safety net covers all investors. Introducing a wedge between identical investors is inefficient. Without commitment, an optimally designed financial safety net covers only a subset of investors. Compared to an economy where all investors are protected, this results in more liquid portfolios, better social insurance, and higher ex ante welfare. Our result can rationalize the prevalent limited coverage of safety nets, such as the lender of last resort facilities.
    Keywords: Bailouts; Safety nets; Time inconsistency; Public liquidity provision
    JEL: E58 E61 G28
    Date: 2016–08–25
  50. By: Cai, Yifei
    Abstract: 本文从理论上分析了货币增速剪刀差对股票市场的作用机制,同时借助于基于滚动窗口拔靴 法的格兰杰因果检验实证检验了两者之间的时变因果关系。实证结果表明两者具有显著的时 变格兰杰因果关系。通常情况下,货币增速剪刀差的增加能够刺激股票市场的繁荣,存在正 向的格兰杰因果关系。但在2015年的“牛市”期间,货币增速剪刀差是股市收益率的负 向格兰杰因果关系。自2015年10月以来,尽管货币剪刀差持续攀升,但实证结果表明 剪刀差的增加仅能提供较弱的相关关系的证据,而不存在因果关系。
    Keywords: 货币增速剪刀差,股市收益率,滚动窗口拔靴法的格兰杰因果关系检验
    JEL: C12 E42 E51 O16
    Date: 2016–08–28
  51. By: Thomas Winberry (University of Chicago); Pablo Ottonello (University of Michigan)
    Abstract: Aggregate investment is central to the conduct of monetary both empirically and theoretically. However, existing studies largely ignore the extensive micro-level heterogeneity in investment behavior across firms. In this paper, we reassess the investment channel of monetary policy in the presence of this heterogeneity by empirically studying the response of micro-level investment to monetary shocks, and using these estimates to build a quantitative heterogeneous firm model for policy analysis. Our empirical analysis combines firm-level Compustat investment data with a high-frequency identification of monetary policy shocks. We will then build a quantitative general equilibrium model incorporating the relevant sources of heterogeneity identified in the data. We will use the model to study two broad issues. First, we will compute the average aggregate effect of monetary policy shocks, and compare the results to the estimated VAR literature. Second, we will study how the distribution of underlying heterogeneity shapes the response to monetary shocks at different points in time. How does the aggregate effect of monetary policy depend on the distribution of productivity, capital, or net worth? Does it vary substantially over the cycle? What does all this imply for the design of monetary policy going forward?
    Date: 2016
  52. By: Trezzi, R.; Porcelli, F.
    Abstract: A law issued to allocate reconstruction grants following the 2009 "Aquilano" earthquake has resulted in a large and unanticipated discontinuity across municipalities with comparable damages. Using diff-in-diff analysis we estimate the "local spending" and the "local tax" multipliers--according to the composition of the stimulus--controlling for the negative supply shock generated by the event. The stimulus prevented a fall in economic activity and the multiplicative effects of tax cuts are estimated much higher than those of spending. Our results underline the importance of countercyclical fiscal interventions and suggest the most effective composition of such a stimulus.
    Keywords: Natural disasters, fiscal multipliers, Mercalli scale
    JEL: C36 E62 H70
    Date: 2016–08–24
  53. By: Tamilselvan, M.; Manikandan, S.
    Abstract: The review of various literatures and renowned publications is emphasizing that the gross domestic production of a nation is determined by several factors such as growth in agriculture and manufacturing sector, export, inflation, exchange rate and international investment. In spite of different factors affecting the growth, the incremental growth of foreign direct investment in various sectors is considered to be a vital factor which controls all other factor. The 1991 new economic policy has unfolded red carpet to the international investors and reduced the uncertainty on the legal and regulatory frame work boosted the investors’ confidence in the economy. As a result, the Indian economy witnessed a vigorous growth since the implementation of Liberalization, Privatization and Globalization (LPG). In this regard this paper is attempting to investigate the contribution of foreign direct investment to the gross domestic production of India. The investigation was made using a simple regression between foreign direct investment (FDI) and gross domestic production (GDP) for 23 years from 1991 – 2014. The result revealed that FDI has as positive impact on GDP.
    Keywords: FDI, Growth, GDP,
    JEL: E2 F3
    Date: 2015–10–24
  54. By: Anis Chowdhury
    Abstract: This paper provides a review of various sources of finance for poverty reduction. Some salient findings are: declining significance of aid, especially for middle-income countries; aid remains a major source of finance for LDCs; improved government revenue efforts in most developing countries. The paper also highlights revenue losses through trade liberalization, corporate tax concessions and through illicit flows of funds, and provides empirical evidence to debunk negative views about counter-cyclical macroeconomic policies. Some key recommendations are: countries should examine the costs and benefits of corporate tax concessions and the equity impact of indirect taxations, such as value added tax; strengthen tax administration; enhance tax progressivity; international and regional tax cooperation; fulfilling aid commitment and ensuring additionality of aid while dealing with humanitarian crises and climate change.
    Keywords: poverty, vulnerability; aid (ODA); tax revenue; social protection; counter-cyclical policies
    JEL: E5 E6 F35 H2 H6 I3 O23
    Date: 2016
  55. By: Krzysztof Czerkas (CERFIN)
    Abstract: According to the Polish Banking Supervision (KNF) statistics , banking mortgage loans total 22.4% of the overall assets of the Polish banking credit portfolio as of January 2015 data. Around 15% of those loans might have Loan To Property Value proportion (LTV) exceeding 100%. Rapid growth of mortgage loans in Poland was the result of growth of foreign currency mortgage loans. That was the typical situation for Central and Eastern European Countries. In the Polish banking sector, collective provisions typical for retail segment including IBNR (Identified but nor Reported Loss Provisions) provision applied under current IAS 39 seems to be too low to cover credit risk. Assuming conservative credit risk parameters in determining collective provisions might affect adversely the financial situation of the Polish banks. The financial results of the Polish banks might go down considerably. In addition, borrowers having loans denominated in foreign currency have won certain legal cases in Europe and in Poland. Therefore, the presence of the foreign currency denominated mortgage loans (FCML) in the Polish banking sector represents an element of the systemic risk and has to be solved. Growth of FCML weakens the abilities of central banks to control over credit creation and to steer monetary policy. On the other hand, compulsory conversion of the mortgage loans hitherto denominated in foreign currency into mortgage loans in Polish zloty by special law, might create considerable losses in the Polish banking sector and reduction of Capital Adequacy Ratio of the Polish banks. Therefore, direct replication of the Hungarian style legal solution of the foreign currency banking (compulsory loan currency conversion into PLN) loans might create problems in the Polish banking sector. Moreover, potential state aid devoted to some borrowers in Poland that have saved significant amount of money having cheaper foreign currency mortgage loans is controversial from the social justice point of view. Law should not treat favourable one group of borrowers at the expense of the another one. Any legal solutions to sort out the problem of foreign currency loans have to take these facts into consideration. Given that background, the paper present several ways to resolve the problem of the foreign currency loans in the Polish banking sector. Complex solution how to tackle FCML problem in the Polish banking sector is needed. Any realistic solution is going to be is linked with extra provisioning after thorough investigation of real nature of FCML borrowers in Poland (households) and their financial standing. Polish Banking Supervision (KNF) should play a leading role in initiating such survey leading to more credible credit risk parameters in IAS 39.
    Keywords: financial stability, banking supervision, financial crisis, exchange rate risk
    JEL: G28 G01 F34 E44
    Date: 2016–08
  56. By: Tetsuo Ono (Graduate School of Economics, Osaka University); Yuki Uchida (Graduate School of Economics, Osaka University)
    Abstract: This study considers public education policy and its impact on growth and wel- fare across generations. In particular, the study compares two scal perspectives| tax nance and debt nance|and shows that in a competitive equilibrium context, the growth and utility in the debt- nance case could be higher than those in the tax- nance case in the long run. However, the opposite occurs when the policy is shaped by politics. When the degree of parents' altruism is low, they choose debt nance in their voting, despite its long-run worse performance because a current generation can pass the cost of debt repayment to future generations.
    Keywords: Economic growth, Human capital, Public debt, Political equilib- rium
    JEL: D70 E24 H63
    Date: 2016–01
  57. By: Corsetti, G.; Mavroeidi, E.; Thwaites, G.; Wolf, M.
    Abstract: We study how small open economies can engineer an escape from deflation and unemployment in a global secular stagnation. Building on the framework of Eggertsson et al. (2016), we show that the transition to full employment requires a dynamic depreciation of the exchange rate, without prejudice for domestic inflation targeting. However, if depreciation has strong income and valuation effects, the escape can be beggar thy self, raising employment but actually lowering welfare. We show that, while a relaxation in the Effective Lower Bound (ELB) can work as a means of raising employment and inflation in financially closed economies, it may have exactly the opposite effect when economies are financially open.
    Keywords: Small open economy, secular stagnation, capital controls, optimal policy, zero lower bound
    JEL: F41 E62
    Date: 2016–08–15
  58. By: Laurent Bouton; Alessandro Lizzeri; Nicola Persico
    Abstract: This paper presents a dynamic political-economic model of total government obligations. Its focus is on the interplay between debt and entitlements. In our model, both are tools by which temporarily powerful groups can extract resources from groups that will be powerful in the future: debt transfers resources across periods; entitlements directly target the future allocation of resources. We prove five main results. First, debt and entitlement are strategic substitutes in the sense that constraining debt increases entitlements (and vice versa). Second, if entitlements are unconstrained, it is sometimes welfare-improving to relax debt constraints, even in the absence of shocks that require smoothing. This is because borrowing constraints lead to higher entitlement spending and reduces overall provision of public goods. Third, equilibrium entitlements are excessive from a utilitarian perspective because they transfer resources to powerful agents who are already in a privileged position. However, very tight constraints in entitlements limit agents' opportunities to smooth consumption. Fourth, debt and entitlements respond in opposite ways to political instability and, in contrast with prior literature, political instability may even reduce debt when entitlements are endogenous. Finally, we identify a possible explanation for the joint growth of debt and entitlements.
    JEL: D72 E62 H60
    Date: 2016–08
  59. By: Tansel, Aysit (Middle East Technical University); Ozdemir, Zeynel Abidin (Gazi University); Aksoy, Emre (Kirikkale University)
    Abstract: This article explores the long-run relationship between unemployment rate and labor force participation rate in Canada. The cointegration analysis vindicates the existence of a long-run relationship between these two variables. This finding leads us to doubt the pertinence of the unemployment invariance hypothesis for Canada. This is consistent with the empirical studies for Japan, Sweden and the United States, but contradicts the empirical studies for Australia, Romania and Turkey. There are contradictory studies for the United Kingdom.
    Keywords: unemployment invariance hypothesis, unemployment, labor force participation, cointegration, Canada
    JEL: E24 J64 J21
    Date: 2016–08
  60. By: Bouton, Laurent; Lizzeri, Alessandro; Persico, Nicola
    Abstract: This paper presents a dynamic political-economic model of total government obligations. Its focus is on the interplay between debt and entitlements. In our model, both are tools by which temporarily powerful groups can extract resources from groups that will be powerful in the future: debt transfers resources across periods; entitlements directly target the future allocation of resources. We prove five main results. First, debt and entitlement are strategic substitutes in the sense that constraining debt increases entitlements (and vice versa). Second, if entitlements are unconstrained, it is sometimes beneficial not to constrain debt (even in the absence of shocks that require smoothing). Third, if debt is unconstrained, it is beneficial to limit entitlements but not to eliminate them. Fourth, debt and entitlements respond in opposite ways to political instability and, in contrast with prior literature, political instability may even reduce debt when entitlements are endogenous. Finally, we identify a possible explanation for the joint growth of debt and entitlements.
    Keywords: entitlement programs; fiscal rules; Government Debt; political economy
    JEL: D72 E62 H60
    Date: 2016–08
  61. By: Kirk Hamilton; John F. Helliwell; Michael Woolcock
    Abstract: We combine theory with data from different domains to provide an empirical analysis of the scale and variability of social capital as wealth. This is used to argue, given what we have learned in the literature on social capital, that the welfare returns to investing in trust could be substantial. Using social trust data from 132 nations covered by the Gallup World Poll, we present a range of estimates of social trust’s wealth-equivalent values. The estimates of the wealth embodied in social capital are very large, and with a structure and distribution quite different from those for physical capital. These estimates reflect values above and beyond what social trust contributes to supporting incomes and health. Although social trust is an important component of total wealth in all regions and country groupings, there are nonetheless big variations within and among regions, ranging from as low as 12% of total wealth in Latin America to 28% in the OECD.
    JEL: E21 E22 I31
    Date: 2016–08
  62. By: Potter, Simon M. (Federal Reserve Bank of New York)
    Abstract: Remarks at the 2016 Economic Policy Symposium at Jackson Hole, Wyoming.
    Keywords: stigma; discount window; zero bound; balance sheet normalization; moral hazard; market discipline
    Date: 2016–08–26
  63. By: Porzecanski, Arturo C.
    Abstract: Sovereign debt restructurings may experience marginal changes as a result of recent modifications in contractual terms being incorporated into new bond issues, but for the most part they will likely resemble what has generally worked so well in recent decades to the satisfaction of most governments and private creditors. The statutory reforms that have been proposed to date are highly unlikely to gain traction for a variety of reasons, including the prospect that they would have been stymied when confronted with a rogue sovereign debtor such as Argentina.
    Keywords: Argentina, default, debt, sovereign, restructuring, statutory; contractual; collective action; pari passu; finance
    JEL: E6 F3 F34 F51 F65 H63 K4 N26
    Date: 2016–08
  64. By: Bick, Alexander (Arizona State University); Brüggemann, Bettina (McMaster University); Fuchs-Schündeln, Nicola (Goethe University Frankfurt)
    Abstract: We use national labor force surveys from 1983 through 2011 to construct hours worked per person on the aggregate level and for different demographic groups for 18 European countries and the US. We find that Europeans work 19% fewer hours than US citizens. Differences in weeks worked and in the educational composition each account for one third to one half of this gap. Lower hours per person than in the US are in addition driven by lower weekly hours worked in Scandinavia and Western Europe, but by lower employment rates in Eastern and Southern Europe.
    Keywords: labor supply, employment, hours worked, Europe-US hours gap, demographic structure
    JEL: E24 J21 J22
    Date: 2016–08
  65. By: Ellen E. Meade; Nicholas A. Burk; Melanie Josselyn
    Abstract: May 26, 2015{{p}}The FOMC meeting minutes: An assessment of counting words and the diversity of views{{p}}Ellen E. Meade, Nicholas A. Burk, and Melanie Josselyn1{{p}}The Federal Reserve's communications with the public have evolved substantially since the early 1990s and today include: policy{{p}}statements released shortly after the conclusion of monetary policy meetings; minutes of those meetings issued three weeks later;{{p}}quarterly economic forecasts from the members of the Federal Reserve Board of Governors and the presidents of the Federal Reserve{{p}}Banks; the Chair's press conferences four times per year; a semi-annual Monetary Policy Report that is submitted to the Congress and{{p}}released to the public, along with the Chair's testimony on that report; and transcripts of monetary policy meetings published after five{{p}}years. In addition, the public websites maintained by the Board and the 12 Federal Reserve Banks provide reports, testimony, speeches,{{p}}and a wealth of other information on the policy and operations of the Federal Reserve System.{{p}}In this note, we focus on the minutes of Federal Open Market Committee (FOMC) meetings. Summaries of FOMC meetings have been{{p}}released to the public in some form since 1936; detailed minutes like those available today have been released since 1993. Between{{p}}1993 and the end of 2004, minutes for each meeting were released after the subsequent meeting and so did not inform the public about{{p}}the most recent thinking or discussions of the FOMC. In December 2004, the Committee decided to begin publishing the minutes 21{{p}}days after each FOMC meeting, shortening the publication lag by several weeks and providing the public with more timely information{{p}}about policy deliberations and the rationale for policy decisions.{{p}}The minutes provide a detailed summary of the discussion at an FOMC meeting.2 Typically, an FOMC meeting begins with a staff review{{p}}of foreign currency and domestic open market operations over the intermeeting period; meeting participants have the opportunity to{{p}}question the staff on these market developments. Next comes the "economic go-round," which begins with several staff presentations on{{p}}developments in, and prospects for, the economies of the United States and foreign countries. After asking any questions they may have{{p}}on those presentations, all meeting participants--the Federal Reserve governors and the presidents of the 12 Federal Reserve Banks--{{p}}discuss their views on economic developments and the outlook. Generally, Reserve Bank presidents include in their remarks{{p}}commentary from industry contacts and information about recent economic developments in their Districts. In addition, four times each{{p}}year, the economic go-round includes a staff summary of the economic projections submitted by FOMC participants.3{{p}}A go-round on monetary policy follows the economic go-round. A staff briefing outlines policy options and discusses several alternatives{{p}}for the statement that the Committee will issue after the meeting. Following questions, the governors and Reserve Bank presidents{{p}}outline their views on policy and discuss possible amendments to the language in drafts of the Committee's statement. At the conclusion{{p}}of that discussion, the Committee members vote on the policy that will be followed over the period until the next FOMC meeting and on{{p}}the statement that will be released shortly after the meeting is adjourned. A key feature of the governance of Federal Reserve monetary{{p}}policy decisions is that, while all Reserve Bank presidents participate in the discussion at FOMC meetings, not all are voting members of{{p}}the Committee. The Federal Reserve Act specifies that Committee "members"--that is, those who vote on policy--include all Federal{{p}}Reserve governors, the President of the Federal Reserve Bank of New York, and four of the other 11 Federal Reserve Bank presidents{{p}}who vote according to a specified rotation.{{p}}In addition to the review of financial market developments and the go-rounds on the economy and monetary policy, many FOMC{{p}}meetings include discussion of a special topic. In recent years, for example, meeting participants have discussed the Federal Reserve's{{p}}large-scale asset purchase programs, the forward guidance about future policy that is contained in the Committee's postmeeting{{p}}statement, and the consensus statement on the Committee's longer-run goals and monetary policy strategy that was first issued in{{p}}January 2012. The minutes of FOMC meetings summarize participants' discussions of such special topics as well as their comments{{p}}during the regular go-rounds.{{p}}The role of the minutes and the postmeeting statement{{p}}Transcripts from FOMC meetings around the time that the Committee decided to expedite the release of the minutes shed some light on{{p}}the role that policymakers see the minutes playing in their communications about monetary policy, particularly relative to the postmeeting{{p}}statement that the Committee releases to the public shortly after the conclusion of each FOMC meeting. In May 2005, Reserve Bank{{p}}Presidents Guynn (Atlanta) and Poole (St. Louis) noted their increasing discomfort with the fact that the statement did not reflect the{{p}}widespread uncertainty that meeting participants were expressing about policy at that meeting. They wanted that range of uncertainty to{{p}}be presented in the statement, "because, otherwise, the statement would not truly reflect what happened at the meeting."4 Chairman{{p}}Greenspan responded that "what is in the minutes, even though they are released later, reflects what occurred before we voted on the{{p}}statement. So whatever the Committee votes is the Committee's view at that point."5 At a meeting in 2006, Chairman Bernanke framed{{p}}the relative roles of the statement and the minutes slightly differently, saying, "... I do not think that the minutes and the statement are{{p}}perfect substitutes. The statement, after all, is much more timely, and it represents something closer to a consensus or median view of{{p}} FRB: FEDS Notes: The FOMC meeting minutes: An assessment of coun...{{p}}1 of 5 5/26/2015 1:37 PM{{p}}Table 1. Quantitative words used in the FOMC{{p}}minutes{{p}}"all"{{p}}"all but one"{{p}}"almost all"{{p}}"most"{{p}}"many"{{p}}"several"{{p}}"few"{{p}}"a couple" or "two"{{p}}"one" ... "another{{p}} Source: Deborah J. Danker and Matthew M. Luecke (2005),{{p}}"Background on FOMC Meeting Minutes," Federal Reserve{{p}}Bulletin, Spring, pp. 175-179.{{p}}the Committee as opposed to the minutes, which try to express the range of views and discussion around the table."6 These extracts{{p}}from FOMC transcripts suggest that policymakers see the minutes as providing insight about the breadth of views that the postmeeting{{p}}statement does not provide.{{p}}All of the minutes released since the end of 2004 on the expedited schedule have reflected not only views expressed by Committee{{p}}"members" but also those articulated by meeting "participants," a larger group that includes those Reserve Bank presidents who do not{{p}}vote. The minutes' account of the economic go-round summarizes the views expressed by participants, while the section titled{{p}}"Committee Policy Action" includes only the members because they are responsible for the policy decision. At times since 2008, the{{p}}minutes have included participants' views on particular aspects of monetary policy when the range of views expressed by participants{{p}}was broader than that expressed by the members alone.7{{p}}Another way in which the meeting minutes are informative about the diversity of views is through the use of "quantitative" or "counting"{{p}}words--such as "few" or "many"--to characterize the number of members or participants aligned with a particular view. In their article on{{p}}the minutes, Danker and Luecke (2005) provide a list of the quantitative words, which are shown on Table 1 ordered from largest to{{p}}smallest. We add to this list two other counting words that are used with some frequency in the minutes: "some" and "a number of."{{p}}An analysis of the "counting" words{{p}}The question we are interested in is, do the FOMC minutes present diverse{{p}}perspectives and has that diversity changed over time? To find the answer, we{{p}}subject the FOMC minutes released from 2005 through 2014 to some{{p}}elementary text analysis and provide an assessment of the dispersion of{{p}}viewpoints expressed through an examination of the quantitative words. The{{p}}frequency of these counting words can be used as a measure of the range of{{p}}opinion expressed at an FOMC meeting.{{p}}For the purposes of this analysis, we examined all sections of the minutes that{{p}}describe policymakers' discussion at the meeting; these are, primarily, the{{p}}sections on the economic and monetary policy go-rounds titled "Participants'{{p}}Views on Current Conditions and the Economic Outlook" and "Committee Policy{{p}}Action," respectively, although we also examined any special sections or{{p}}additional paragraphs that included sentences pertaining to views expressed by{{p}}participants.8{{p}}Before turning to our findings, we note a few details about our counting procedures. First, statements that represent unanimity are{{p}}sometimes expressed explicitly, as in "all members judged;" at other times, statements of near unanimity are implicit, as in "participants{{p}}judged" or "the Committee judged." In making the counts, we grouped these explicit and implicit statements together with statements{{p}}using "all but one" and "almost all" to form a grouping that reflects a high degree of consensus.9 Second, the quantitative word "one" is{{p}}sometimes used in a "one ... another" construction; when that occurred, we counted both "one" and "another" as separate views. Finally,{{p}}we counted only expressions of views or opinions, and not mere discussions of a topic.{{p}}It is important to note that the FOMC minutes have gotten considerably longer over time. In 2005, the first year that the minutes were{{p}}released on their current, expedited schedule, the average length per meeting was just under 4200 words.10 By 2014, the word count{{p}}had risen to around 8350, with the bulk of this rise occurring between 2005 and 2009. The total annual frequency of the counting words{{p}}from participants' and members' paragraphs of the minutes has risen over time as well, particularly since about 2011 (chart 1).{{p}}Interestingly, the counting words have risen as a share in the total number of words (chart 2), pointing either to a greater diversity of{{p}}viewpoints or to more complete reporting of the diversity of views.{{p}}Chart 1: Total quantitative words, FOMC minutes, 2005-2014{{p}} FRB: FEDS Notes: The FOMC meeting minutes: An assessment of coun...{{p}}2 of 5 5/26/2015 1:37 PM{{p}}Accessible version{{p}}Chart 2: Quantitative words as a share of total words, annual average{{p}}Accessible version{{p}}The total number of paragraphs that reflect views expressed by members is substantially smaller than the total number of paragraphs{{p}}describing participants' views--sometimes substantially so. Thus, it is not surprising that the total count for the quantitative words is{{p}}higher for participants' paragraphs than for members' paragraphs (not shown). Since 2007, the quantitative words in participants'{{p}}paragraphs of the minutes have accounted for about 75 to 80 percent of all quantitative words used.{{p}}Chart 3 provides the share of each counting word in the total for participants (upper panel) and members (lower panel) for each year.{{p}}Highly consensual statements (shown as "consensus") comprised nearly 60 percent of participants' paragraphs in 2005, compared with{{p}}about 25 percent in 2014. Members' paragraphs have a higher proportion of "consensus" statements, which is to be expected in light of{{p}}the role that the Committee's statement and the discussion of it plays in the members' paragraphs. In 2014, "consensus" statements{{p}}constituted 80 percent of members' paragraphs, although for several years prior the share was somewhat lower--generally between 60{{p}}and 70 percent. A wider diversity of opinion among participants and members might be expected in the aftermath of the financial crisis{{p}}given that, after the Committee reduced the target for the federal funds rates to nearly zero in December 2008, the Federal Reserve{{p}}began using nontraditional monetary policy--namely, large-scale asset purchases and forward guidance--to provide additional stimulus{{p}} FRB: FEDS Notes: The FOMC meeting minutes: An assessment of coun...{{p}}3 of 5 5/26/2015 1:37 PM{{p}}to the economy. These nontraditional monetary policies were less familiar and were therefore less well understood. Policymakers{{p}}debated their efficacy and costs and spent a great deal of time at FOMC meetings discussing the implications and effects of them. In{{p}}addition, the greater diversity of viewpoints could reflect a changing composition of policymakers with different opinions.11{{p}}Chart 3: Shares of counting words in total, FOMC participants (upper) and members (lower){{p}}Accessible version{{p}}In conclusion, the FOMC meeting minutes are a key means for informing the Congress and the public about the full range of{{p}}policymaker opinion and debate about monetary policy issues, and thus help ensure that the Federal Reserve is accountable to the{{p}}Congress and the public. Since 2005, the minutes have gotten considerably longer. Our analysis of the quantitative words used in the{{p}}minutes indicates that the meeting minutes capture a wide diversity of viewpoints expressed by FOMC policymakers and that this{{p}}diversity appears to have increased over time, particularly since the financial crisis.{{p}}1. The authors thank Bill English, Thomas Laubach, Steve Meyer, and Bob Tetlow for comments. Return to text{{p}}2. For a more detailed discussion of what happens at an FOMC meeting, see the speech "Come with Me to the FOMC" that Governor Elizabeth A. Duke gave{{p}}to the Money Marketeers of New York University in October 2010. Return to text{{p}}3. These projections have been collected since October 2007. Tables and charts of the projections are released to the public prior to the Chair's press{{p}} FRB: FEDS Notes: The FOMC meeting minutes: An assessment of coun...{{p}}4 of 5 5/26/2015 1:37 PM{{p}}Accessibility Contact Us Disclaimer Website Policies FOIA PDF Reader{{p}}conference and a detailed analysis is released three weeks later in the "Summary of Economic Projections" section of the minutes for the relevant FOMC{{p}}meetings. Return to text{{p}}4. President Poole, Transcript of FOMC meeting, May 3, 2005, p. 86. Return to text{{p}}5. Chairman Greenspan, Transcript of FOMC meeting, May 3, 2005, p. 86. Return to text{{p}}6. Chairman Bernanke, Transcript of FOMC meeting, March 27-28, 2006, p. 140. Return to text{{p}}7. For example, in the minutes of the January 2013 meeting, there were three paragraphs at the end of the economic go-round section on the benefits and{{p}}costs of the Committee's open-ended asset purchase program, the pace of asset purchases, and the economic thresholds in the Committee's forward{{p}}guidance. On some occasions, there have been separate sections in the minutes pertaining to a discussion of aspects of monetary policy; see, for example,{{p}}the section titled "Policy Planning" in the minutes of the October 2013 meeting which relates participants' views on the strategy and tactics of future policy.{{p}}Return to text{{p}}8. The "Committee Policy Action" section reflects views expressed by members; it does not reflect views expressed only by those participants who are not{{p}}voting members. Return to text{{p}}9. We also included statements using the construct "participants generally judged..." or "members generally judged..." in this grouping. Return to text{{p}}10. The word counts and other analysis in this section include scheduled meetings and exclude video conference (unscheduled) meetings. The minutes of the{{p}}first meeting in a year are significantly longer than subsequent meetings owing to discussion of procedural matters. Return to text{{p}}11. For example, since the beginning of 2005, there has been considerable turnover among Federal Reserve governors: Six governors appointed prior to 2005{{p}}resigned and, of the 12 governors appointed since 2005, only five of them remain.Return to text{{p}} Disclaimer: FEDS Notes are articles in which Board economists offer their own views and present analysis on a range of topics in{{p}}economics and finance. These articles are shorter and less technically oriented than FEDS Working Papers.{{p}}Last update: May 26, 2015{{p}}Home | Economic Research & Data{{p}} FRB: FEDS Notes: The FOMC meeting minutes: An assessment of coun...{{p}}5 of 5 5/26/2015 1:37 PM
    Date: 2015–05–26
  66. By: Porcelli, F.; Trezzi, R.
    Abstract: Although earthquakes are large idiosyncratic shocks for affected regions, little is known of their impact on economic activity. Seismic events are rare, the data is crude (the Richter scale measures the magnitude but says nothing of the associated damages) and counterfactuals are often entirely absent. We suggest an innovative identification strategy to address these issues based on the so-called ’Mercalli scale’ ranks - a geophysical methodology devised to gauge seismic damages relying on a newly compiled dataset following 95 Italian provinces from 1986 to 2011 (including 22 seismic episodes) offering an ideal ground for identification. Also, we carry out counterfactuals taking advantage of ex ante identical neighboring provinces that only differ ex post in terms of damages. Contrary to conventional views, we find that the impact of seismic events on output is negligible (or even positive) including after the most devastating events.
    Keywords: Natural disasters, Mercalli scale
    JEL: Q54 E00 J01
    Date: 2016–08–24
  67. By: Boubaker, Sabri; Gounopoulos, Dimitrios; Nguyen, Duc Khuong; Paltalidis, Nikos
    Abstract: US public pension funds deficits remain stubbornly high even though market conditions have improved in the post-crisis period. This article examines the role of lower short- and long-term interest rates imposed by the use of unconventional monetary policy on pension funds risk taking and asset allocation behavior. We quantify the effects of the Zero Lower Bound policy and the launch of unconventional monetary policy measures by using two structural Vector AutoRegression (VAR) models, a Bayesian VAR and a Markov switching-structural VAR. We provide the first comprehensive evidence showing that persistently low interest rates and falling Treasury yields cause a substantial increase in pension funds risk and portfolios beta. Additionally, we document that the severe funding shortfall in many pension schemes is, to a large extent, associated with and prompted by changes in the monetary policy framework.
    Keywords: Pension funds; Unconventional monetary policy; Asset allocation; Zero lower bound
    JEL: E52 G11 G23
    Date: 2015–05
  68. By: Chiu, Ching-Wai (Jeremy) (Bank of England); Hacioglu Hoke, Sinem (Bank of England)
    Abstract: Motivated by the desire to probe macroeconomic tail events and to capture non-linear economic dynamics, we estimate two types of regime switching models: threshold VAR and Markov switching VAR. For each of the models, we estimate regimes which carry the interpretation of recessionary/normal and financially stressful/stable periods. Using the recursiveness assumption and conditional on shocks of one standard deviation, we show that (i) financial shocks hitting during times of recessions create disproportionately more severe contractions in output; (ii) output growth shocks hitting in financially stressful times result in disproportionately further financial stress; (iii) monetary policy shocks hitting in recessionary times create more severe contractions in output. We also demonstrate the power of a feedback loop between real and financial sectors when extremely large shocks hit the economy in normal/financially stable periods. Afterwards, we perform out-of-sample forecasting exercises, and find that the threshold VAR model has the potential to predict tail events in conditional forecasting. Overall, our findings provide strong evidence of nonlinearities and shock amplification mechanisms in the UK data, as well as empirical support to the theoretical findings of Brunnermeier and Sannikov (2014).
    Keywords: Macroeconomic tail events; nonlinear VARs; generalised impulse response functions; density forecasts
    JEL: C31 C53 E32 G01
    Date: 2016–08–19
  69. By: Peter Hördahl; Jhuvesh Sobrun; Philip Turner
    Abstract: International linkages between interest rates in different currencies are strong, and ultra-low rates have become a global phenomenon. This paper compares how interest rates in advanced economies and in emerging economies are conditioned by two global benchmarks - the Federal funds rate at the short end and the "world" real interest rate at the long end. Real equilibrium policy rates (the natural rate) have fallen in many countries, and short-term rates worldwide have been further depressed by many years of the US policy rate close to zero. Nevertheless, changes in the Federal funds rate have less effect on longer-term rates, and thus on financing conditions, than is often supposed. The decline in the world long-term rate since 2008 has been driven almost entirely by a fall in the world term premium (negative in nominal terms since mid-2014). The world short-term rate expected over the long run has fallen only modestly over the past seven years or so, and is now just over 2% (compared with around 4% pre-Lehman).
    Keywords: bond markets, financial globalization, natural rate of interest, term premium and shadow policy rate
    Date: 2016–08
  70. By: Yi Huang (The Graduate Institute, Geneva); Marco Pagano (Università di Napoli Federico II, CSEF, EIEF, CEPR and ECGI); Ug Panizza (The Graduate Institute, Geneva); Tano Santos
    Abstract: In China, local public debt issuance between 2006 and 2013 crowded out investment by private manufacturing firms by tightening their funding constraints, while it did not affect state-owned and foreign firms. Using novel data for local public debt issuance, we establish this result in three ways. First, local public debt is inversely correlated with the city-level investment ratio of domestic private manufacturing firms. Instrumental variable regressions indicate that this link is causal. Second, local public debt has a larger negative effect on investment by private firms in industries more dependent on external funding. Finally, in cities with high government debt, firm-level investment is more sensitive to internal funding, also when this sensitivity is estimated jointly with the firm’s likelihood of being credit-constrained. Altogether, these results suggest that, by curtailing private investment, the massive public debt issuance associated with the post-2008 fiscal stimulus sapped long-term growth prospects in China.
    Keywords: Investment, Local public debt, Crowding out, Credit constraints, China
    JEL: E22 H63 H74 L60 O16
    Date: 2016–07–30
  71. By: Ana Paola Cruz Rodríguez; Alvin Alejandro Olarte
    Abstract: La presente investigación tiene como objetivo analizar si tasas mayores de sanción inciden en el comportamiento de evasión fiscal de los declarantes. Para lograr tal cometido, a partir de la metodología experimental, se obtiene una primera aproximación que procede a examinar los datos provenientes de una muestra de estudiantes universitarios. El diseño del experimento introduce los costes psicológicos de la estructura clásica asociados al incumplimiento por medio de un procedimiento de inspección y sanción en dos tipos de tratamiento: un Sistema Débil (SD) con una tasa de sanción menor a la de un Sistema Fuerte (SF), aunque con igual probabilidad de inspección en los dos. Los resultados sugieren que no existe una diferencia significativa entre los montos declarados entre un sistema y otro, lo que concuerda con una tendencia a la evasión en el país, tema ya abordado por diferentes estudios. Sin embargo, el aporte individual total, que se compone de declaraciones y sanciones, sí es significativamente mayor en el SF, aspecto tocado superficialmente en las recomendaciones dadas por la Comisión de Expertos para la Equidad y Competitividad Tributaria, quienes proponen básicamente crear un código tributario, ampliar la base y aumentar las tasas impositivas.
    Keywords: evasión, sanción, economía experimental, política fiscal.
    JEL: C91 E62 H26
    Date: 2016–08–26
  72. By: Monica Billio; Roberto Casarin; Luca Rossini
    Abstract: Seemingly unrelated regression (SUR) models are used in studying the interactions among economic variables of interest. In a high dimensional setting and when applied to large panel of time series, these models have a large number of parameters to be estimated and suffer from inferential problems. We propose a Bayesian nonparametric hierarchical model for multivariate time series in order to avoid the overparametrization and overfitting issues and to allow for shrinkage toward multiple prior means with unknown location, scale and shape parameters. We propose a two-stage hierarchical prior distribution. The first stage of the hierarchy consists in a lasso conditionally independent prior distribution of the Normal-Gamma family for the SUR coefficients. The second stage is given by a random mixture distribution for the Normal-Gamma hyperparameters, which allows for parameter parsimony through two components. The first one is a random Dirac point-mass distribution, which induces sparsity in the SUR coefficients; the second is a Dirichlet process prior, which allows for clustering of the SUR coefficients. We provide a Gibbs sampler for posterior approximations based on introduction of auxiliary variables. Some simulated examples show the efficiency of the proposed methods. We study the effectiveness of our model and inference approach with an application to macroeconomics.
    Keywords: Bayesian nonparametrics; Large VAR; MCMC; Mixture model; Dirichlet Process; Graphical Representation
    JEL: C11 C13 C14 C32 C51 E17
    Date: 2016
  73. By: Shuping Shi (Macquarie University); Stan Hurn (QUT); Peter C B Phillips (Yale University)
    Abstract: This paper re-examines changes in the causal link between money and income in the United States for over the past half century (1959 - 2014). Three methods for the data-driven discovery of change points in causal relationships are proposed, all of which can be implemented without prior detrending of the data. These methods are a forward recursive algorithm, a recursive rolling algorithm and the rolling window algorithm all of which utilize subsample tests of Granger causality within a lag-augmented vector autoregressive framework. The limit distributions for these subsample Wald tests are provided. The results from a suite of simulation experiments suggest that the rolling window algorithm provides the most reliable results, followed by the recursive rolling method. The forward expanding window procedure is shown to have worst performance. All three approaches find evidence of money-income causality during the Volcker period in the 1980s. The rolling and recursive rolling algorithms detect two additional causality episodes: the turbulent period of late 1960s and the starting period of the subprime mortgage crisis in 2007.
    Keywords: Time-varying Granger causality, subsample Wald tests, Money-Income
    JEL: C12 C15 C32 E47
    Date: 2016–08–30
  74. By: Ewa Karwowski; Engelbert Stockhammer
    Abstract: Financialisation research has originally focussed on the US experience, but the concept is now increasingly applied to emerging economies (EMEs). There is a rich literature stressing peculiarities of individual country experiences, but little systematic comparison across EMEs. This paper fills this gap, providing an overview of the debate and identifying six financialisation interpretations for EMEs. These different interpretations stress (1) financial deregulation (2) foreign financial inflows, (3) asset price volatility, (4) the shift from bank-based to market-based finance, (5) business debt, and (6) household indebtedness. We construct and compare measures of the six financialisation interpretations across a sample of 17 EMEs from Latin America, emerging Europe, Africa and Asia, contrasting them with the US and UK, two financialised economies. We find considerable variation in financialisation experiences of EMEs. Asset price volatility is found across continents. Asia has been more exposed to capital inflows, stock markets have gained importance and private sector debt risen. In emerging Europe financial deregulation has been more pronounced with lower levels but strong increases in household debt. The picture is similar in South Africa, the African EME in the sample, where household debt is comparatively high. Financialisation in Latin America is weaker according to our measures.
    Keywords: financialisation, emerging markets, financial instability, asset price volatility, heterodox economics
    JEL: B50 E30 F34 G01 G12 G15
    Date: 2016–08
  75. By: Robert F. Martin; Tenyanna Munyan; Beth Anne Wilson
    Abstract: November 12, 2014{{p}}Potential Output and Recessions: Are We Fooling Ourselves?{{p}}{{p}}Robert F. Martin, Teyanna Munyan, and Beth Anne Wilson 1{{p}}{{p}}The economic collapse in the wake of the global financial crises (GFC) and the weaker-than-expected recovery in many countries have led to questions about the impact of severe downturns on economic potential. Indeed, for several major economies, the level of output is nowhere near returning to pre-crisis trend (figure 1). Such developments have resulted in repeated downward revisions to estimates of potential output by private- and public-sector forecasters. In addition, this disappointment in post-recession growth has contributed to concerns that the U.S. economy, among others, is entering an era of secular stagnation. However, the historical experience of advanced economies around recessions indicates that the current experience is less unusual than one might think. First, output typically does not return to pre-crisis trend following recessions, especially deep ones. Second, in response, forecasters repeatedly revise down measures of trend.{{p}}Figure 1{{p}}Figure 1{{p}}{{p}}Accessible Version{{p}}{{p}}We use quarterly real GDP data for 23 advanced economies from around 1970 to present.2 Applying a standard recession dating technique, we identify 149 recessions (117 recessions if the Great Recession is excluded).3 We calculate pre-recession trend growth as the four-year average growth rate for each country, two years prior to each peak, and examine GDP as a percentage of this trend for each recession in our sample prior to the Great Recessions. We take care to calculate trend output in a way such that it is not influenced by the pace of output following the cyclical peak. We exclude the two years prior to the peak to prevent periods of potentially "bubble-like" growth from boosting our trend. That said, our results are robust to other definitions of trend and to estimates of potential output derived from a growth accounting framework.{{p}}{{p}}The black line in figure 2 shows the level of real GDP as a percent of its pre-recession trend around all 117 non-GFC recessions. We also calculate output performance relative to trend for two subsets of recessions--very mild and very severe.4 On average, GDP remains well below its previous trend, even for short and shallow recessions. Deep and long recessions, of course, lead to the largest cumulative output loss.5 Table 1 shows average growth rates before and after recessions, including GFC recessions. If actual growth returned to pre-crisis trend then growth immediately following recessions would be higher than average to make up the gap. In fact, the average growth in the four years after the recession trough is generally lower than prior to the pre-recession peak.{{p}}Figure 2{{p}}Figure 2. Recoveries in the Advanced Economies. GDP trend calculated as exponential function growing at the four-year average two years prior to the peak Severe recessions are in the top 25th percent of recessions as measured by both depth and duration. Similarly, mild recessions are in the bottom 25th percentile of each category.{{p}}{{p}}Accessible Version{{p}}Table 1{{p}}Advanced Economy GDP{{p}}Percent Change, a.r.{{p}}6-2 yrs prior to peak 4 yrs post-trough p-value*{{p}}All Recessions ex. GFC (n=116) 3.7 3.1 0.02*{{p}}Severe (n=14) 3.9 3.6 0.61{{p}}Mild (n=16) 3.3 2.1 0.02*{{p}}All others (n=86) 3.7 3.2 0.10{{p}}{{p}}* Significant at the 95 percent level.{{p}}{{p}}{{p}}Economic models usually assume that recession-induced gaps will close over time, typically via a period of above trend growth. In our results, growth is not faster after the recession than before, implying that the recession-induced gap is closed primarily by revising estimates of trend output growth lower. Interestingly, much of the downward revision to estimates of trend output happens well into the recovery. In particular, as economies recover and the lower level of actual output persists, potential output is gradually revised down toward actual GDP.{{p}}{{p}}One reason output gaps are typically viewed as transitory is likely that techniques to calculate trend are often two sided, bending in response to the evolution of actual data. Figure 3 illustrates this phenomenon using U.S. real GDP data before and after the 2009 recession. As GDP declines during the recession, trend output, as measured here using a Hodrick-Prescott (HP) filter, gradually moderates toward actual output--reducing the negative gap between actual and trend data around the recession and increasing the positive gap prior to the start of the recession. Economists and forecasting institutions calculating trend using HP-filtered data in 2007 had a very different sense of the cyclical position compared to their impression now for that same year.{{p}}Figure 3{{p}}Figure 3. Real U.S. GDP and HP Filtered GDP. Source: OECD Economic Outlook, various vintages of actual and projected. Trends are calculated by applying an HP-filter to vintages of OECD GDP series. The trend is then extrapolated toward using the growth rate implied by the last year of HP-filtered data. The level of actual GDP is indexed to 100 in 2005.{{p}}{{p}}Accessible Version{{p}}{{p}}This pattern of revision also holds true if potential is calculated using a growth accounting framework, the method used by policymaking institutions such as the OECD. To see how estimates of potential using this methodology are adjusted around turning points, we use projections from the OECD's bi-annual economic outlook for 62 recessions from 1989 to 2009 in 23 advanced economies and construct a database of various vintages of the OECD's estimates of potential growth--i.e. forecasts made a year prior to the recession trough, at the trough, and three years after the trough. Figure 4 shows these vintages averaged around recession troughs. These data reveal a pattern of downward revisions to the level of potential around turning points. Even three years post-trough, potential growth is still being revised down. This same pattern of systematic underestimation of the impact of recessions on potential and the subsequent downward revision of potential output holds true for other policymaking institutions in the wake of the Great Recession. While it is tempting to attribute this to the impact of the financial crisis on growth, the discussion above suggests that this pattern is long standing. Ironically, despite being known as the dismal science, economists may be too optimistic about the recovery path of output following recessions.{{p}}Figure 4{{p}}Figure 4. Potential GDP Forecasts (various vintages). Source: OECD Economic Outlook. Data are for 23 countries covering 62 recessions from 1989-2009. Lines represent the median growth rates, forecast and history, of each country/recession pair for the period closest to the pre-recession peak, recession trough, and 2 years past-trough.{{p}}{{p}}Accessible Version{{p}}{{p}}Although these calculations are simple, they raise deeper questions about the impact of recessions on trend output. The finding that recessions tend to depress the long-run level of output may imply that demand shocks have permanent effects. The sustained deviation of the level of output from pre-crisis trend points to flaws in the way the economics profession models the recovery of output to economic shocks and raises further doubts about the reliance on measures of output gaps to determine economic slack. For policymakers, the results also point to the cost of recessions, especially deep and long ones, and provide a rationale for strong and rapid policy responses to economic downturns.{{p}}{{p}}{{p}}{{p}}{{p}}Bibliography{{p}}{{p}}Cerra, Valerie and Sweta Chaman Saxena. 2008. "Growth Dynamics: The Myth of Economic Recovery." American Economic Review, 98(1): 439-457.{{p}}{{p}}Haltmaier, Jane (2012) "Do Recessions Affect Potential Output?" International Finance Discussion Paper, 1066. (PDF){{p}}{{p}}Harding, Don and Adrian Rodney Pagan. 2002. "Dissecting the Cycle: a Methodological Investigation." Journal of Monetary Economics, 49(2): pp 365-381.{{p}}{{p}}Howard, Greg, Robert F. Martin, and Beth Anne Wilson. 2011. "Are Recoveries from Banking and Financial Crises Really So Different?" International Finance Discussion Paper, 1037.{{p}}{{p}}Oulton, Nicholas and Sebastiá-Barriel, María. 2013. "Long and Short-Term Effects of the Financial Crisis on Labour Productivity, Capital and Output." CEP Discussion Papers, CEPDP1185. Centre for Economic Performance, London School of Economics and Political Science, London, UK.{{p}}{{p}}{{p}}{{p}}{{p}}1. Robert F. Martin ( is Chief of Global Monetary and Sovereign Markets, Teyanna Munyan is a Senior Research Assistant in Advanced Foreign Economies, and Beth Anne Wilson ( is an Associate Director in the Division of International Finance, Board of Governors of the Federal Reserve System, Washington, D.C. 20551 U.S.A. We thank Gregory Howard for contributions, Andrew Brooks for excellent assistance, and Matteo Iacoviello and participants at workshops within the Federal Reserve Board for their comments. The views in this paper are solely the responsibility of the authors and should not be interpreted as reflecting the views of the Board of Governors of the Federal Reserve System or of any other person associated with the Federal Reserve System. Return to text{{p}}{{p}}2. The 23 countries are: Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan, Luxembourg, Netherlands, Norway, New Zealand, Portugal, Sweden, Spain, Switzerland, United Kingdom, and United States. Return to text{{p}}{{p}}3. We identify recessions using the Bry-Boschen procedure (BBQ) for quarterly data as described by Harding and Pagan (2002). The BBQ method identifies cyclical peaks and troughs as local maxima in the two quarters preceding and the two quarters following. It then eliminates maxima that do not alternate between peaks and troughs or do not have a long enough time span, in this case two quarters from a peak to trough and four quarters from a trough to peak. Once these criteria are met, recessions are defined as the time between a peak and a trough. Return to text{{p}}{{p}}4. Mild recessions are those in the bottom 25th percentile in terms of distance from pre-recession peak and duration of output decline. Severe recessions are those in the top 25th percentile of recession depth and duration. For more details, see Howard et al. (2011). Return to text{{p}}{{p}}5. See also Cerra and Saxena (2008), Haltmaier (2012), and Oulton and Sebastiá-Barriel (2013) which find similar long-run effects on output from deep recessions. Varying the specification of our regression or the definition of pre-crisis trend can modify these loss estimates, but our results all suggest a sustained hit to output, especially from severe recessions. Not surprisingly, including the Great Recession strengthens the results. Return to text{{p}}{{p}} Disclaimer: IFDP Notes are articles in which Board economists offer their own views and present analysis on a range of topics in economics and finance. These articles are shorter and less technically oriented than IFDP Working Papers.{{p}}Search Working Papers{{p}}{{p}}{{p}}{{p}}Skip Meet Economists Section{{p}}{{p}}Meet the Economists{{p}}All Economists{{p}}By Field of Interest{{p}}Financial Economics{{p}}International Economics{{p}}Macroeconomics{{p}}Mathematical and Quantitative Methods{{p}}Microeconomics{{p}}{{p}}Skip stay connected section{{p}}{{p}}Stay Connected{{p}}Twitter{{p}}YouTube{{p}}RSS Feeds{{p}}Subscribe{{p}}{{p}}{{p}}Last update: November 12, 2014
    Date: 2014–11–12
  76. By: Silvia Delrio
    Abstract: This paper investigates the effects of a global uncertainty shock in open economies and the role of country relative risk exposure in the transmission of the shock. We employ an Interacted VAR model to take the time- varying dimension of country relative risk exposure into account. Evidence of nonlinearities in the real effects of a global uncertainty shock is found. The reduction in real activity is larger when the country is more exposed to aggregate risk. These findings support recent theoretical contributions on the role of risk exposure in the transmission of uncertainty shocks.
    Keywords: Global uncertainty shocks, Country relative riskiness, International analysis, Interacted VAR, Generalized Impulse Response Functions
    JEL: C32 E32 F41
    Date: 2016
  77. By: Barnett, Richard (School of Economics Drexel University); Bhattacharya, Joydeep (Department of Economics Iowa State University); Bunzel, Helle (Department of Economics Iowa State University)
    Abstract: This paper studies the fight-or-flight ambivalence people show towards the success of the proverbial Joneses. If an agent cares about leisure and his consumption relative to a benchmark set by the Joneses, his preferences display the keeping-up-with-the-Joneses (KUJ) property if an increase in the benchmark urges him to substitute away from leisure into work, allowing him to finance more consumption; the opposite is labeled running-away-from-the-Joneses (RAJ). The long literature, thus far, assumes these definitions apply globally: if any agent’s preferences display KUJ (or RAJ), everyone’s will. In an otherwise-standard environment with endowment heterogeneity, we showcase the possibility that these definitions may apply locally but not globally. This means heterogeneous agents sharing the same underlying preferences may respond very differently to the Joneses: while some may choose to keep up, others, possibly their close neighbors, may choose to run away. Depending on the benchmark, the same agent may exhibit KUJ and RAJ on different parts of the consumption space. The analysis is novel because a) such fight-or-flight conflict does not arise in existing models of consumption externalities, b) it arises endogenously here, and c) it exposes a deep connection between the fight-or flight-response and wealth-dependent risk aversion of agents and explains the behavior in terms of textbook income/substitution effects.
    Keywords: leisure distribution; rat race; wealth-dependent risk aversion; keeping up with the Joneses; income inequality
    JEL: E20 I31 J22
    Date: 2016–08–31
  78. By: Mathieu Lefebvre; Sergio Perelman
    Abstract: It has been long suggested that public pension wealth may crowd out household savings. However, there remains controversy about the extent of this displacement effect. In this paper we use an original microsimulation model based on retrospective survey data collected through the third wave of the Survey of Health, Ageing and Retirement in Europe (SHARE) to estimate the displacement effect of public pension wealth on other wealth in Belgium. Combining this rich dataset with an accurate estimation of the individual pension entitlements allows us to circumvent some of the main measurement errors problems faced by previous studies. We estimate that an extra euro of public pension wealth is associated with about 14-25 cent decline in non-pension wealth.
    Keywords: Social security, saving, microsimulation, crowding-out effect.
    JEL: D91 H55 E21 J14
    Date: 2016
  79. By: Porzecanski, Arturo C.
    Abstract: The voluminous and protracted litigation and arbitration saga featuring the Republic of Argentina (mostly as defendant or respondent, respectively) established important legal and arbitral precedents, as illustrated by three cases involving Argentina which were appealed all the way up to the U.S. Supreme Court and were settled in 2014. At first glance, the scale of Argentina-related litigation activity might be explained by the sheer size of the government’s 2001 default, the world’s largest-ever up to that point. However, its true origins were the unusually coercive, aggressive way that the authorities in that country went about defaulting on, and restructuring, their sovereign debt obligations, as well as the radical, seemingly irreversible changes to the “rules of the game” affecting foreign strategic investors, which broke binding commitments prior governments had made in multiple bilateral investment treaties.
    Keywords: Argentina, FSIA, sovereign, default, expropriation, restructuring, New York, ICSID
    JEL: E6 F3 F34 F5 F51 F65 H63 K4 N26
    Date: 2016–08
  80. By: Vasilev, Aleksandar
    Abstract: This note describes the lottery- and insurance-market equilibrium in an economy with non-convex straight-time and overtime employment. In contrast to Hansen and Sargent (1988), the overtime-decision is a sequential one. This requires two separate insurance market to operate, one for straight-time work, and one for overtime. In addi- tion, given that the labor choice for regular and overtime hours is made in succession, the insurance market for overtime needs to open once the insurance market has closed. This segmentation and sequentiality of insurance markets operation is a new result in the literature and a direct consequence of the sequential nature of the overtime labor decision.
    Keywords: indivisible labor,straight-time,overtime,sequential lotteries,insurance
    JEL: E1 J2
    Date: 2016
  81. By: Groll, Dominik; Monacelli, Tommaso
    Abstract: The desirability of flexible exchange rates is a central tenet in international macroeconomics. We show that, with forward-looking staggered pricing, this result crucially depends on the monetary authority's ability to commit. Under full commitment, flexible exchange rates generally dominate a monetary union (or fixed exchange rate) regime. Under discretion, this result is overturned: a monetary union dominates flexible exchange rates. By fixing the nominal exchange rate, a benevolent monetary authority finds it welfare improving to trade off flexibility in the adjustment of the terms of trade in order to improve on its ability to manage the private sector's expectations. Thus, inertia in the terms of trade (induced by a fixed exchange rate) is a cost under commitment, whereas it is a benefit under discretion, for it acts like a commitment device.
    Keywords: monetary union,flexible exchange rates,commitment,discretion,welfare losses,nominal rigidities
    JEL: E52 F33 F41
    Date: 2016
  82. By: César Huaroto De la Cruz (Especialista en el Proyecto EduLab en el Ministerio de Educación del Perú y Profesor Contratado de la Pontificia Universidad Católica del Perú (PUCP).); Arturo Leonardo Vásquez Cordano (Oficina de Estudios Económicos -Osinergmin, Vice-Presidente de la Comisión de Libre Competencia de Indecopi, Profesor de la Escuela de Postgrado GERENS y del Departamento de Economía de la PUCP)
    Abstract: El presente estudio tiene como propósito evaluar el efecto derivado de los conflictos socio-ambientales (CSA) en el Perú en el valor de las acciones de las empresas mineras que cotizan en la Bolsa de Valores de Lima, como una primera aproximación a la medición del impacto económico de los conflictos sociales que afectan al sector minero. La hipótesis central del documento que será evaluada y contrastada con evidencia del caso peruano es que la aparición de los conflictos sociales puede tener un efecto negativo en las decisiones de invertir en las acciones de las empresas mineras involucradas y, a su vez, que la aparición y fin de los conflictos generan cambios en las estrategias de inversión que se reflejan en variaciones en el valor de las acciones, en general como respuesta a la mayor (o menor) incertidumbre respecto al futuro de estas inversiones tal como predice la teoría moderna de la inversión basada en las opciones reales (Dixit y Pindyck, 1994; Trigeorgis, 1996). A pesar de que se encuentra evidencia de una correlación negativa entre la aparición de los CSA y la caída en la rentabilidad mensual de las acciones, ésta no es estadísticamente significativa al incluir errores estándar robustos a la presencia de heterocedasticidad y autocorrelación. Sin embargo, sí se encuentra evidencia de un incremento en el número de operaciones de compra-venta de las acciones cuando los conflictos culminan y un aumento de la variabilidad del precio en el mes en que el conflicto culmina y, en sentido opuesto, cuando éste se inicia. La principal conclusión del documento es que los CSA afectan al mercado bursátil a través de incrementos en la incertidumbre, en este caso, sobre la rentabilidad a futuro de los proyectos mineros que son sujetos de conflicto con las comunidades aledañas a estos proyectos, corroborando la hipótesis principal de la teoría de las opciones reales. Si bien esto no se materializa en un impacto en la rentabilidad, sí parece ser señal de que la aparición de conflictos vuelve cautelosos a los inversionistas, los cuales optan por esperar a que la incertidumbre se resuelva antes de apostar de nuevo por inversiones en acciones mineras (respaldadas por portafolios de activos reales como los proyectos mineros). En contraste, al final de los conflictos, la incertidumbre se resuelve, normalizando la situación del mercado bursátil, lo cual brinda mayor seguridad a los inversionistas para invertir en acciones mineras.
    JEL: Q34 D74 D81 G14 E22
    Date: 2015–06

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