nep-for New Economics Papers
on Forecasting
Issue of 2013‒06‒09
fifteen papers chosen by
Rob J Hyndman
Monash University

  1. Do central banks’ forecasts take into account public opinion and views? By Ricardo Nunes
  2. Unpredictability in Economic Analysis, Econometric Modeling and Forecasting By David Hendry; Grayham E. Mizon
  3. The Role of Data Revisions and Disagreement in Professional Forecasts By Eva A. Arnold
  4. Understanding bond risk premia By Pavol Povala; Anna Cieslak
  5. An Empirical BVAR-DSGE Model of the Australian Economy By Sean Langcake; Tim Robinson
  6. Inference and forecasting in the age-period-cohort model with unknown exposure with an application to mesothelioma mortality By Bent Nielsen; Maria Dolores Martinez Miranda; Jens Perch Nielsen
  7. Bond returns and market expectations By Carlo Altavilla; Raffaella Giacomini; Riccardo Costantini
  8. Modelling and Simulation: An Overview By Michael McAleer; Felix Chan; Les Oxley
  9. Modelling farm structural change: A feasibility study for ex-post modelling utilizing FADN and FSS data in Germany and developing an ex-ante forecast module for the CAPRI farm type layer baseline By Alexander Gocht; Norbert Röder; Sebastian Neuenfeldt; Hugo Storm; Thomas Heckelei
  10. Global Banks, Financial Shocks and International Business Cycles: Evidence from Estimated Models By Robert Kollmann
  11. Optimism bias in project appraisal: deception or selection? By Eliasson, Jonas; Fosgerau, Mogens
  12. Price jump prediction in a limit order book By Ban Zheng; Eric Moulines; Frédéric Abergel
  13. Credit ratings and bank monitoring ability By Leonard I. Nakamura; Kasper Roszbach.
  14. Kondratieff Waves in the World System Perspective By Korotayev Andrey; Grinin Leonid
  15. Bayesian generalized additive models for location, scale and shape for zero-inflated and overdispersed count data By Nadja Klein; Thomas Kneib; Stefan Lang

  1. By: Ricardo Nunes
    Abstract: The Federal Reserve through the Federal Open Market Committee (FOMC) regularly releases macroeconomic forecasts to the general public and the US congress with the purpose of explaining the likely evolution of the economy and the appropriate stance of monetary policy. Immediately before doing so, the FOMC receives a forecast produced by the Federal Reserve staff which remains private for five years. The literature has pointed out that, despite the informational advantage of the FOMC, its forecast differs from and is not always more accurate than the staff forecast. This finding has raised concerns regarding the loss of relevant information and the usefulness of the FOMC forecasts. This paper brings evidence that the FOMC forecast also incorporates other publicly available forecasts and views, and that the weight attributed to public forecasts is larger than what is optimal given a mean squared error objective. These findings are consistent with i) the institutional role of the FOMC in being representative of a variety of public views, ii) the academic literature recommendation to use equal weights and not to overfit specific forecasts based on past performance. The statistical model can also account for several empirical regularities of the forecasts.
    Date: 2013
  2. By: David Hendry; Grayham E. Mizon
    Abstract: Unpredictability arises from intrinsic stochastic variation, unexpected instances of outliers, and unanticipated extrinsic shifts of distributions.  We analyze their properties, relationships, and different effects on the three arenas in the title, which suggests considering three associated information sets.  The implications of unanticipated shifts for forecasting, economic analyses of efficient markets, conditional expectations, and inter-temporal derivations are described.  The potential success of general-to-specific model selection in tackling location shifts by impulse-indicator saturation is contrasted with the major difficulties confronting forecasting.
    Keywords: Unpredictability, 'Black Swans', distributional shifts, forecast failure, model selection, conditional expectations
    JEL: C51 C22
    Date: 2013–03–14
  3. By: Eva A. Arnold (Universität Hamburg (University of Hamburg))
    Abstract: This paper aims at evaluating individual expectation accuracy of professional forecasters for 57 U.S., European, and German macroeconomic indicators over the period 1999-2010. The empirical analysis shows that initial announcements are partly considerably revised, and that some revisions occur systematically. Taking into account whether announcements are revised systematically and whether economists (assumingly) aim at forecasting the initial release or the latest revision, signicant dierences can be observed with regard to forecasters' expectation errors. In general, forecasters that are (assumingly) aiming to predict the latest revisions of German indicators are able to form better forecasts if these indicators are revised systematically. Though to a lower extent, this relationship is also observable regarding U.S. indicators. Forecasters' disagreement about fundamentals is higher during recessions and when stock markets are volatile.
    Keywords: Rational expectations, Macroeconomic indicators, Disagreement, Survey analysis, Real-time data
    JEL: D81 D84 E17
    Date: 2013–05
  4. By: Pavol Povala (University of Lugano); Anna Cieslak (Northwestern University)
    Abstract: We decompose yields into long-horizon expected inflation and maturity-related cycles to study the predictability of bond excess returns. Cycles capture the risk premium and the business cycle variation of short rate expectations. From cycles, we construct a forecasting factor that explains up to above 50% (30%) of in-sample (out-of-sample) variation of annual bond returns. The factor varies at a frequency higher than the business cycle, and predicts real activity at long horizons. It also aggregates information from different macro-finance predictors of bond returns. Our decomposition reveals why bond returns are predictable by a linear combination of forward rates or the term spread.
    Date: 2012
  5. By: Sean Langcake (Reserve Bank of Australia); Tim Robinson (Reserve Bank of Australia)
    Abstract: In this paper, we develop a multi-sector dynamic stochastic general equilibrium (DSGE) model with a simple commodity sector and assess whether forecasts from this model can be improved by using it as a prior for an empirical Bayesian vector autoregression (BVAR). We treat the world economy as being observed and exogenous to the small economy, rather than unobserved, as has been done in some previous studies, such as Hodge, Robinson and Stuart (2008) and Lees, Matheson and Smith (2011). We find that the forecasts from a BVAR that uses this DSGE model as a prior are generally more accurate than those from the DSGE model alone. Nevertheless, these forecasts do not outperform a small open economy VAR estimated using other standard priors or simple univariate benchmarks.
    Keywords: empirical Bayesian VAR; forecasting; small open economy
    JEL: C53 E13
    Date: 2013–06
  6. By: Bent Nielsen; Maria Dolores Martinez Miranda; Jens Perch Nielsen
    Abstract: It is of considerable interest to forecast future mesothelioma mortality.  No measures for exposure are available so it is not straight forward to apply a dose-response model.  It is proposed to model the counts of deaths directly using a Poisson regression with an age-period-cohort structure, but without offset.  Traditionally the age-period-cohort is viewed to suffer from an identification problem.  It is shown how to re-parameterize the model in terms of freely varying parameters, so as to avoid this problem.  It is shown how to conduct inference and how to construct distribution forecasts.
    Date: 2013–03–26
  7. By: Carlo Altavilla; Raffaella Giacomini (Institute for Fiscal Studies and UCL); Riccardo Costantini
    Abstract: A well-documented empirical result is that market expectations extracted from futures contracts on the federal funds rate are among the best predictors for the future course of monetary policy. We show how this information can be exploited to produce accurate forecasts of bond excess returns and to construct profitable investment strategies in bond markets. We use a tilting method for incorporating market expectations into forecasts from a standard term-structure model and then derive the implied forecasts for bond excess returns. We find that the method delivers substantial improvements in out-of-sample accuracy relative to a number of benchmarks. The accuracy improvements are both statistically and economically significant and robust across a number of maturities and forecast horizons. The method would have allowed an investor to obtain positive cumulative excess returns from simple "riding the yield curve" investment strategies over the past ten years, and in this respect it would have outperformed its competitors even after accounting for a risk-return tradeoff.
    Keywords: Yield curve modelling, futures, market timing, exponential tilting, Kullback-Leibler
    Date: 2013–05
  8. By: Michael McAleer (Econometric Institute Erasmus School of Economics Erasmus University Rotterdam and Tinbergen Institute The Netherlands and Department of Quantitative Economics Complutense University of Madrid Spain and Institute of Economic Research Kyoto University Japan); Felix Chan (School of Economics and Finance Curtin University Australia); Les Oxley (Department of Economics University of Waikato New Zealand)
    Abstract: The papers in this special issue of Mathematics and Computers in Simulation cover the following topics: improving judgmental adjustment of model-based forecasts, whether forecast updates are progressive, on a constrained mixture vector autoregressive model, whether all estimators are born equal: the empirical properties of some estimators of long memory, characterising trader manipulation in a limit-order driven market, measuring bias in a term-structure model of commodity prices through the comparison of simultaneous and sequential estimation, modelling tail credit risk using transition matrices, evaluation of the DPC-based inclusive payment system in Japan for cataract operations by a new model, the matching of lead underwriters and issuing firms in the Japanese corporate bond market, stochastic life table forecasting: a time-simultaneous fan chart application, adaptive survey designs for sampling rare and clustered populations, income distribution inequality, globalization, and innovation: a general equilibrium simulation, whether exchange rates affect consumer prices: a comparative analysis for Australia, China and India, the impacts of exchange rates on Australia's domestic and outbound travel markets, clean development mechanism in China: regional distribution and prospects, design and implementation of a Web-based groundwater data management system, the impact of serial correlation on testing for structural change in binary choice model: Monte Carlo evidence, and coercive journal self citations, impact factor, journal influence and article influence.
    Keywords: Modelling, simulation, forecasting, time series models, trading, credit risk, empirical finance, health economics, sampling, groundwater systems, exchange rates, structural change, citations.
    JEL: C15 C63 E27 E37 E47 F37 F47
    Date: 2013–05
  9. By: Alexander Gocht (Johann Heinrich von Thünen-Institut); Norbert Röder (Johann Heinrich von Thünen-Institut); Sebastian Neuenfeldt (Johann Heinrich von Thünen-Institut); Hugo Storm (Bonn University); Thomas Heckelei (Bonn University)
    Abstract: The present study aims to develop a prototype analytical tool to assess structural changes at the farm level in EU-27 using the Farm Accountancy Data Network (FADN) combined with the Farm Structure Survey (FSS). For the purpose of this study, farm structural change is related to the change in production systems, therefore a change in farm size and farm entry/exit into one sector/farm typology. In the ex-post analysis of structural change two methodologies are presented, one in which structural change is analysed from a discrete perspective using a Markov approach, whereas the second uses the continuous perspective to evaluate the type of farming over time using MCI (Multiplicative Competitive Interaction) models. The methodolgies are applied in selected German regions and the goodness of fit in the out of sample prediction is compared. In the ex-ante methodology, the existing farm module of CAPRI (Common Agricultural Policy Regionalised Impact System) is expanded by considering the findings of the statistical ex-post-analysis when projecting farm-type structural change in the baseline trends. Results show that the Markov prediction may outperform naïve prediction methods but that the quality of the prediction is critically dependent on the model specification. A higher in-sample fit does not necessarily lead to better out-of-sample prediction, which potentially indicates that the effects of specific explanatory variables may change over time. In addition, introducing structural change into the CAPRI farm type baseline improve policy impact assessment and hence a more reliable and consistent farm grid for simulations is constructed.
    Keywords: structural change, FADN, FSS, Markov Chain, MCI model, CAPRI
    JEL: C51 C52 C54 Q19
    Date: 2012–10
  10. By: Robert Kollmann (ECARES, Université Libre de Bruxelles a)
    Abstract: This paper takes a two-country model with a global bank to US and Euro Area (EA) data. The estimation results (based on Bayesian methods) suggest that global banking strengthens the positive international transmission of real economic disturbances. Shocks that originate in the banking sector account for roughly 20% of the forecast error variance of investment, and about 5% of the forecast variance of US and EA GDP. Bank shocks explain 5%-20% of the fall in US and EA real activity, during the Great Recession.
    Date: 2012
  11. By: Eliasson, Jonas (KTH Royal Institute of Technology); Fosgerau, Mogens (DTU Transport)
    Abstract: A number of highly cited papers by Flyvbjerg and associates have shown that ex-ante infrastructure appraisals tend to be overly optimistic. Ex post evaluations indicate a bias where investment cost is higher and demand lower on average than predicted ex ante. These authors argue that the bias must be attributed to intentional misrepresentation by project developers. This paper shows that the bias may arise simply as a selection bias, without there being any bias at all in predictions ex ante, and that such a bias is bound to arise whenever ex ante predictions are related to the decisions whether to implement projects. Using a database of projects we present examples indicating that the selection bias may be substantial. The examples also indicate that benefit-cost ratios remains a useful selection criterion even when cost and benefits are highly uncertain, gainsaying the argument that such uncertainties render cost-benefit analyses useless.
    Keywords: Cost overruns; Forecast accuracy; Cost-benefit analysis; Appraisal; Selection bias; Winner’s curse
    JEL: R40 R42
    Date: 2013–06–03
  12. By: Ban Zheng (LTCI - Laboratoire Traitement et Communication de l'Information [Paris] - Télécom ParisTech - CNRS : UMR5141, FiQuant - Chaire de finance quantitative - Ecole Centrale Paris); Eric Moulines (LTCI - Laboratoire Traitement et Communication de l'Information [Paris] - Télécom ParisTech - CNRS : UMR5141); Frédéric Abergel (FiQuant - Chaire de finance quantitative - Ecole Centrale Paris, MAS - Mathématiques Appliquées aux Systèmes - EA 4037 - Ecole Centrale Paris)
    Abstract: A limit order book provides information on available limit order prices and their volumes. Based on these quantities, we give an empirical result on the relationship between the bid-ask liquidity balance and trade sign and we show that liquidity balance on best bid/best ask is quite informative for predicting the future market order's direction. Moreover, we de ne price jump as a sell (buy) market order arrival which is executed at a price which is smaller (larger) than the best bid (best ask) price at the moment just after the precedent market order arrival. Features are then extracted related to limit order volumes, limit order price gaps, market order information and limit order event information. Logistic regression is applied to predict the price jump from the limit order book's feature. LASSO logistic regression is introduced to help us make variable selection from which we are capable to highlight the importance of di erent features in predicting the future price jump. In order to get rid of the intraday data seasonality, the analysis is based on two separated datasets: morning dataset and afternoon dataset. Based on an analysis on forty largest French stocks of CAC40, we nd that trade sign and market order size as well as the liquidity on the best bid (best ask) are consistently informative for predicting the incoming price jump.
    Keywords: limit order book, price jumps, predictibility, LASSO,
    Date: 2013–05
  13. By: Leonard I. Nakamura; Kasper Roszbach.
    Abstract: In this paper we use credit rating data from two large Swedish banks to elicit evidence on banks’ loan monitoring ability. For these banks, our tests reveal that banks’ credit ratings indeed include valuable private information from monitoring, as theory suggests. However, our tests also reveal that publicly available information from a credit bureau is not efficiently impounded in the bank ratings: The credit bureau ratings not only predict future movements in the bank ratings but also improve forecasts of bankruptcy and loan default. We investigate possible explanations for these findings. Our results are consistent with bank loan officers placing too much weight on their private information, a form of overconfidence. To the extent that overconfidence results in placing too much weight on private information, risk analyses of the bank loan portfolios in our data could be improved by combining the bank credit ratings and public credit bureau ratings. The methods we use represent a new basket of straightforward techniques that enable both financial institutions and regulators to assess the performance of credit rating systems. ; Supersedes Working Paper 10-21.
    Keywords: Credit ratings ; Risk assessment
    Date: 2013
  14. By: Korotayev Andrey; Grinin Leonid
    Abstract: The analysis of long economic cycles allows us to understand long-term world-system dynamics, to develop forecasts, to explain crises of the past, as well as the current global economic crisis. The article offers an historical sketch of research on K-waves; it analyzes the nature of Kondratieff waves that are considered as a special form of cyclical dynamics that emerged in the industrial period of the World System history. It offers a historical and theoretical analysis of K-wave dynamics in the World System framework; in particular, it studies the influence of the long wave dynamics on the changes of the world GDP growth rates during the last two centuries. Special attention is paid to the interaction between Kondratieff waves and Juglar cycles. The article is based on substantial statistical data, it extensively employs quantitative analysis, contains numerous tables and diagrams. On the basis of the proposed analysis it offers some forecasts of the world economic development in the next two decades.
    Keywords: economic cycles, world system, Kondratieff waves, Juglar cycles
    JEL: B1 E3 E6 N1
    Date: 2013–02–28
  15. By: Nadja Klein; Thomas Kneib; Stefan Lang
    Abstract: Frequent problems in applied research that prevent the application of the classical Poisson log-linear model for analyzing count data include overdispersion, an excess of zeros compared to the Poisson distribution, correlated responses, as well as complex predictor structures comprising nonlinear effects of continuous covariates, interactions or spatial effects. We propose a general class of Bayesian generalized additive models for zero-inflated and overdispersed count data within the framework of generalized additive models for location, scale and shape where semiparametric predictors can be specified for several parameters of a count data distribution. As special instances, we consider the zero-inflated Poisson, the negative binomial and the zero-inflated negative binomial distribution as standard options for applied work. The additive predictor specifications rely on basis function approximations for the different types of effects in combination with Gaussian smoothness priors. We develop Bayesian inference based on Markov chain Monte Carlo simulation techniques where suitable proposal densities are constructed based on iteratively weighted least squares approximations to the full conditionals. To ensure practicability of the inference we consider theoretical properties like the involved question whether the joint posterior is proper. The proposed approach is evaluated in simulation studies and applied to count data arising from patent citations and claim frequencies in car insurances. For the comparison of models with respect to the distribution, we consider quantile residuals as an effective graphical device and scoring rules that allow to quantify the predictive ability of the models. The deviance information criterion is used for further model specification.
    Keywords: iteratively weighted least squares, Markov chain Monte Carlo, penalized splines, zero-inflated negative binomial, zero-inflated Poisson
    Date: 2013–06

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