nep-for New Economics Papers
on Forecasting
Issue of 2017‒12‒03
ten papers chosen by
Rob J Hyndman
Monash University

  1. An Empirical Investigation of Direct and Iterated Multistep Conditional Forecasts By McCracken, Michael W.; McGillicuddy, Joseph
  2. Is the market always right? Improving federal funds rate forecasts by adjusting for the term premium By Michael Callaghan
  3. Modeling and forecasting the oil volatility index By Lopes Moreira Da Veiga, María Helena; Gonçalves Mazzeu, Joao Henrique; Mariti, Massimo B.
  4. Performance of Markov-Switching GARCH Model Forecasting Inflation Uncertainty By Raihan, Tasneem
  5. Why Are Inflation Forecasts Sticky? Theory and Application to France and Germany By Frédérique Bec; Raouf Boucekkine; Caroline Jardet
  6. Model risk of risk models By Danielsson, Jon; James, Kevin R.; Valenzuela, Marcela; Zer, Ilknur
  7. Macroeconomic nowcasting and forecasting with big data By Bok, Brandyn; Caratelli, Daniele; Giannone, Domenico; Sbordone, Argia M.; Tambalotti, Andrea
  8. Priors for the long run By Giannone, Domenico; Lenza, Michele; Primiceri, Giorgio E.
  9. The scale of predictability By Bandi, F.M; Perron, B; Tamoni, Andrea; Tebaldi, C.
  10. Predicting returns on asset markets of a small, open economy and the influence of global risks. By David Haab; Thomas Nitschka

  1. By: McCracken, Michael W. (Federal Reserve Bank of St. Louis); McGillicuddy, Joseph (Federal Reserve Bank of St. Louis)
    Abstract: When constructing unconditional point forecasts, both direct- and iterated-multistep (DMS and IMS) approaches are common. However, in the context of producing conditional forecasts, IMS approaches based on vector autoregressions (VAR) are far more common than simpler DMS models. This is despite the fact that there are theoretical reasons to believe that DMS models are more robust to misspecification than are IMS models. In the context of unconditional forecasts, Marcellino, Stock, and Watson (MSW, 2006) investigate the empirical relevance of these theories. In this paper, we extend that work to conditional forecasts. We do so based on linear bivariate and trivariate models estimated using a large dataset of macroeconomic time series. Over comparable samples, our results reinforce those in MSW: the IMS approach is typically a bit better than DMS with significant improvements only at longer horizons. In contrast, when we focus on the Great Moderation sample we find a marked improvement in the DMS approach relative to IMS. The distinction is particularly clear when we forecast nominal rather than real variables where the relative gains can be substantial.
    Keywords: Prediction; forecasting; out-of-sample
    JEL: C12 C32 C52 C53
    Date: 2017–11–01
  2. By: Michael Callaghan (Reserve Bank of New Zealand)
    Abstract: Financial market prices contain valuable information about market participants’ expectations. Information on market participants' expectations of future growth, inflation, and interest rates may help policy-makers reflect on the plausibility of their own forecast assumptions, and understand the likely market reaction to any policy announcement. However, the existence of risk premiums will bias the information content of financial market prices. For interest rate securities, the term premium will create a wedge between market participants’ expectation of the future path of the policy rate and the price being traded.Therefore, in order to extract the ‘true’ underlying policy expectations of market participants, market pricing needs to be adjusted for the term premium. In theory, adjusting for the term premium should improve forecast performance on average, given that it provides an unbiased measure of market participants’ expectations. I therefore use a popular term structure model to test the out-of-sample forecast performance of US market pricing with and without a term premium adjustment. I focus on the short-end of the yield curve, up to two years, as it is directly relevant for policy-makers and financial market commentators. The results suggest that the short-term forecasting performance of US interest rates over the medium term can be improved by adjusting for the term premium in zero-coupon rates and overnight index swap rates. The current negative term premium implies that market participants at present expect the future policy rate in the United States to be higher than that implied by market prices. I also show how the model can be applied to monitor expectations for the future path of the federal funds rate at a daily frequency. The analysis has important implications for policy-makers and financial commentators. Adjusting for the term premium should provide a better measure of market participants’ actual expectations for the future path of the policy rate, and as such can improve forecast performance over the medium term.
    Date: 2017–11
  3. By: Lopes Moreira Da Veiga, María Helena; Gonçalves Mazzeu, Joao Henrique; Mariti, Massimo B.
    Abstract: This paper models and forecasts the crude oil ETF volatility index (OVX). Themotivation lies on the evidence that the OVX has been used in the last years as an important alternative measure to track and analyze the volatility of future oil prices. The analysis of the OVX suggests that it presents similar features to those of the daily market volatility index. The main characteristic is the long range dependence that is modeled either by autoregressive fractional integrated moving averaging (ARFIMA) models or by heterogeneous autoregressive (HAR) specifications. Regarding the latter family of models, we first propose extensions of the HAR model that are based on the net and scale measures of oil prices changes. The aim is to improve the HAR model by including predictors that better capture the impact of oil price changes on the economy. Second, we test the forecasting performance of the new proposals and benchmarks with the model confidence set (MCS) and the Generalized-AutoContouR (G-ACR) tests interms of point forecasts and density forecasting, respectively. Our main findings are as follows: the new asymmetric proposals have superior predictive ability than the heterogeneous autoregressive leverage (HARL) model under two known loss functions. Regarding density forecasting, the best model is the one that includes the scale measureas a proxy of oil price changes and considers a flexible distribution for the errors.
    Keywords: Scale oil price changes; OVX; Net oil price changes; Forecasting OVX; Leverage; Heterogeneous autoregression
    JEL: C53 C52 C51 Q40
    Date: 2017–11
  4. By: Raihan, Tasneem
    Abstract: This paper seeks to uncover the non-linear characteristics of uncertainty underlying the US inflation rates over the period 1971-2015 within a regime-switching framework. Accordingly, we employ two variants of a Markov regime-switching GARCH model, one with normally distributed errors (MS-GARCH-N) and another with t-distributed errors (MS-GARCH-t), and compare their relative in-sample as well as out-of-sample performances with those of their standard single-regime counterparts. Consistent with the findings in existing studies, both of our regime-switching models are successful in identifying the year 1984 as the breakpoint in inflation volatility. Among other interesting results is a new finding that the process of switching to the low volatility regime started around April, 1979 and continued until mid 1983. This time frame is matched with the period of aggressive monetary policy changes implemented by the then Fed chairman Paul Volcker. As regards the performance in forecasting uncertainty, for shorter horizons spanning 1 to 5 months, MS-GARCH-N forecasts are found to outperform all other models whereas for 8 to 12-month ahead forecasts MS-GARCH-t appears superior.
    Keywords: Markov switching, GARCH, inflation uncertainty
    JEL: C01 C53 E31
    Date: 2017–10–31
  5. By: Frédérique Bec (Thema, University of Cergy-Pontoise); Raouf Boucekkine (Aix-Marseille Univ. (Aix-Marseille School of Economics), CNRS, EHESS and Centrale Marseille); Caroline Jardet (Banque de France, DGEI-DCPM, Paris)
    Abstract: This paper proposes a theoretical model of forecasts formation which implies that in presence of information observation and forecasts communication costs, rational professional forecasters might find it optimal not to revise their forecasts continuously, or at any time. The threshold time- and state-dependence of the observation review and forecasts revisions implied by this model are then tested using inflation forecast updates of professional forecasters from recent Consensus Economics panel data for France and Germany. Our empirical results support the presence of both kinds of dependence, as well as their threshold-type shape. They also imply an upper bound of the optimal time between two information observations of about six months and the co-existence of both types of costs, the observation cost being about 1.5 times larger than the communication cost.
    Keywords: forecast revision, binary choice models, information and communication costs
    JEL: C23 D8 E31
    Date: 2017–11
  6. By: Danielsson, Jon; James, Kevin R.; Valenzuela, Marcela; Zer, Ilknur
    Abstract: This paper evaluates the model risk of models used for forecasting systemic and market risk. Model risk, which is the potential for different models to provide inconsistent outcomes, is shown to be increasing with market uncertainty. During calm periods, the underlying risk forecast models produce similar risk readings; hence, model risk is typically negligible. However, the disagreement between the various candidate models increases significantly during market distress, further frustrating the reliability of risk readings. Finally, particular conclusions on the underlying reasons for the high model risk and the implications for practitioners and policy makers are discussed.
    Keywords: model risk; systemic risk; value-at-risk; expected shortfall; Basel III
    JEL: G10 G18 G20 G28 G38
    Date: 2016–02–23
  7. By: Bok, Brandyn (Federal Reserve Bank of New York); Caratelli, Daniele (Federal Reserve Bank of New York); Giannone, Domenico (Federal Reserve Bank of New York); Sbordone, Argia M. (Federal Reserve Bank of New York); Tambalotti, Andrea (Federal Reserve Bank of New York)
    Abstract: Data, data, data . . . Economists know it well, especially when it comes to monitoring macroeconomic conditions—the basis for making informed economic and policy decisions. Handling large and complex data sets was a challenge that macroeconomists engaged in real-time analysis faced long before “big data” became pervasive in other disciplines. We review how methods for tracking economic conditions using big data have evolved over time and explain how econometric techniques have advanced to mimic and automate the best practices of forecasters on trading desks, at central banks, and in other market-monitoring roles. We present in detail the methodology underlying the New York Fed Staff Nowcast, which employs these innovative techniques to produce early estimates of GDP growth, synthesizing a wide range of macroeconomic data as they become available.
    Keywords: monitoring economic conditions; business cycle; macroeconomic data; large data sets; high-dimensional data; real-time data flow; factor model; state space models; Kalman filter
    JEL: C32 C53 E32
    Date: 2017–11–01
  8. By: Giannone, Domenico (Federal Reserve Bank of New York); Lenza, Michele (European Central Bank and ECARES); Primiceri, Giorgio E. (Northwestern University, CEPR, and NBER)
    Abstract: We propose a class of prior distributions that discipline the long-run predictions of vector autoregressions (VARs). These priors can be naturally elicited using economic theory, which provides guidance on the joint dynamics of macroeconomic time series in the long run. Our priors for the long run are conjugate, and can thus be easily implemented using dummy observations and combined with other popular priors. In VARs with standard macroeconomic variables, a prior based on the long-run predictions of a wide class of theoretical models yields substantial improvements in the forecasting performance.
    Keywords: Bayesian vector autoregression; forecasting; overfitting; initial conditions; hierarchical model
    JEL: C11 C32 C33 E37
    Date: 2017–11–01
  9. By: Bandi, F.M; Perron, B; Tamoni, Andrea; Tebaldi, C.
    Abstract: We introduce a new stylized fact: the hump-shaped behavior of slopes and coefficients of determination as a function of the aggregation horizon when running (forward/backward) predictive regressions of future excess market returns onto past economic uncertainty (as proxied by market variance, consumption variance, or economic policy uncertainty). To justify this finding formally, we propose a novel modeling framework in which predictability is specified as a property of low-frequency components of both excess market returns and economic uncertainty. We dub this property scale-specific predictability. We show that classical predictive systems imply restricted forms of scale-specific predictability. We conclude that for certain predictors, like economic uncertainty, the restrictions imposed by classical predictive systems may be excessively strong.
    Keywords: long run; predictability; aggregation; risk-return trade-o�
    JEL: F3 G3
    Date: 2017–07–10
  10. By: David Haab; Thomas Nitschka
    Abstract: Stylized facts of asset return predictability are mainly based on evidence from the US, a large, closed economy, and, hence, are not necessarily representative of small, open economies. Furthermore, discountrate news mainly drive US asset returns. This is not the case in other economies. We use Switzerland as example to highlight the importance of these issues and to assess the impact of global risks on the predictability of asset returns of a small, open economy. We find that the forecast ability of the best Swiss predictive variable varies over time. This time variation is linked to global foreign currency risks.
    Keywords: bond market, business cycle, foreign exchange rate, predictability, risk premium, stock market
    JEL: E32 F31 G15 G17
    Date: 2017

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