nep-for New Economics Papers
on Forecasting
Issue of 2014‒04‒18
fifteen papers chosen by
Rob J Hyndman
Monash University

  1. Selecting and combining experts from survey forecasts By Julieta Fuentes; Pilar Poncela; Julio Rodríguez
  2. Probability and Severity of Recessions By Rachidi Kotchoni; Dalibor Stevanovic
  3. Forecasting Bankruptcy with Incomplete Information By Xu, Xin
  4. Financial Conditions and Density Forecasts for US Output and Inflation By Piergiorgio Alessandri; Haroon Mumtaz
  5. Adaptive forecasting in the presence of recent and ongoing structural change By Giraitis, Liudas; Kapetanios, George; Price, Simon
  6. Generalised density forecast combinations By Fawcett, Nicholas; Kapetanios, George; Mitchell, James; Price, Simon
  7. General correcting formulae for forecasts By Harin, Alexander
  8. Forecasting the Price of Gold Using Dynamic Model Averaging By Goodness C. Aye; Rangan Gupta; Shawkat Hammoudeh; Won Joong Kim
  9. Forecasting financial volatility with combined QML and LAD-ARCH estimators of the GARCH model By Liam Cheung; John Galbraith
  10. Forecasting with the Standardized Self-Perturbed Kalman Filter By Stefano Grassi; Nima Nonejad; Paolo Santucci de Magistris
  11. Macro-Finance Determinants of the Long-Run Stock-Bond Correlation: The DCC-MIDAS Specification By Hossein Asgharian; Charlotte Christiansen; Ai Jun Hou
  12. Fat-tails in VAR Models By Ching-Wai (Jeremy) Chiu; Haroon Mumtaz; Gabor Pinter
  13. Testing for Leverage Effect in Financial Returns By Christophe Chorro; Dominique Guegan; Florian Ielpo; Hanjarivo Lalaharison
  14. The macroeconomic effects of monetary policy: a new measure for the United Kingdom By Cloyne, James; Hürtgen, Patrick
  15. Economic development as major determinant of Olympic medal wins: predicting performances of Russian and Chinese teams at Sochi Games By Wladimir Andreff

  1. By: Julieta Fuentes; Pilar Poncela; Julio Rodríguez
    Abstract: Combining multiple forecasts provides gains in prediction accuracy. Therefore, with the aim of finding an optimal weighting scheme, several combination techniques have been proposed in the forecasting literature. In this paper we propose the use of sparse partial least squares (SPLS) as a method to combine selected individual forecasts from economic surveys. SPLS chooses the forecasters with more predictive power about the target variable, discarding the panelists with redundant information. We employ the Survey of Professional Forecasters dataset to explore the performance of different methods for combining forecasts: average forecasts, trimmed mean, regression based methods and regularized methods also in regression. The results show that selecting and combining forecasts yields to improvements in forecasting accuracy compared to the hard to beat average of forecasters.
    Date: 2014–03
  2. By: Rachidi Kotchoni; Dalibor Stevanovic
    Abstract: This paper advances beyond the prediction of the probability of a recession by also considering its severity in terms of output loss and duration. First, Probit models are used to estimate the probability of a recession at period t + h from the information available at period t. Next, a Vector Autoregression (VAR) augmented with diffusion indices and an inverse Mills ratio (IMR) is fitted to selected measures of real economic activity. The latter model is used to generate two forecasts: an average forecast, and a forecast under the pessimistic assumption that a recession occurs at the forecast horizon. The severity of recessions is then predicted as the gap between these two forecasts. Finally, a zero-inated Poisson model is fitted to historical durations of recessions. Our empirical results suggest that U.S. recessions are fairly predictable, both in terms of occurrence and severity. Out-of-sample experiments suggest that the inclusion of the IMR in the VAR model significantly improves its forecasting performance.
    Keywords: Duration of recessions, Forecasting Real Activity, Probability of Recessions, Probit, Vector Autoregression, Zero Inated Poisson.,
    JEL: C3 C5 C35 E27 E37
    Date: 2013–11–01
  3. By: Xu, Xin
    Abstract: We propose new specifications that explicitly account for information noise in the input data of bankruptcy hazard models. The specifications are motivated by a theory of modeling credit risk with incomplete information (Duffie and Lando [2001]). Based on over 2 million firm-months of data during 1979-2012, we demonstrate that our proposed specifications significantly improve both in-sample model fit and out-of-sample forecasting accuracy. The improvements in forecasting accuracy are persistent throughout the 10-year holdout periods. The improvements are also robust to empirical setup, and are more substantial in cases where information quality is a more serious problem. Our findings provide strong empirical support for using our proposed hazard specifications in credit risk research and industry applications. They also reconcile conflicting empirical results in the literature.
    Keywords: Credit Risk Modeling, Incomplete Information, Hazard Models, Bankruptcy Forecast, Probability of Default (PD), Forecasting Accuracy, Intensity-based Models, Reduced-form Models, Duration Analysis, Survival Analysis
    JEL: C41 G17 G33
    Date: 2013–05–28
  4. By: Piergiorgio Alessandri (Bank of Italy); Haroon Mumtaz (Queen Mary University of London)
    Abstract: When do financial markets help in predicting economic activity? With incomplete markets, the link between financial and real economy is state-dependent and financial indicators may turn out to be useful particularly in forecasting "tail" macroeconomic events. We examine this conjecture by studying Bayesian predictive distributions for output growth and inflation in the US between 1983 and 2012, comparing linear and nonlinear VAR models. We find that financial indicators significantly improve the accuracy of the distributions. Regime-switching models perform better than linear models thanks to their ability to capture changes in the transmission mechanism of financial shocks between good and bad times. Such models could have sent a credible advance warning ahead of the Great Recession. Furthermore, the discrepancies between models are themselves predictable, which allows the forecaster to formulate reasonable real-time guesses on which model is likely to be more accurate in the next future.
    Keywords: Financial frictions, Predictive densities, Great Recession, Threshold VAR
    JEL: C53 E32 E44 G01
    Date: 2014–04
  5. By: Giraitis, Liudas (Queen Mary University of London); Kapetanios, George (Queen Mary University of London); Price, Simon (Bank of England)
    Abstract: We consider time series forecasting in the presence of ongoing structural change where both the time-series dependence and the nature of the structural change are unknown. Methods that downweight older data, such as rolling regressions, forecast averaging over different windows and exponentially weighted moving averages, known to be robust to historical structural change, are found also to be useful in the presence of ongoing structural change in the forecast period. A crucial issue is how to select the degree of downweighting, usually defined by an arbitrary tuning parameter. We make this choice data-dependent by minimising forecast mean square error, and provide a detailed theoretical analysis of our proposal. Monte Carlo results illustrate the methods. We examine their performance on 97 US macro series. Forecasts using data-based tuning of the data discount rate are shown to perform well.
    Keywords: Recent and ongoing structural change; forecast combination; robust forecasts
    JEL: C10 C59
    Date: 2014–03–28
  6. By: Fawcett, Nicholas (Bank of England); Kapetanios, George (Queen Mary University of London); Mitchell, James (WBS); Price, Simon (Bank of England)
    Abstract: Density forecast combinations are becoming increasingly popular as a means of improving forecast ‘accuracy’, as measured by a scoring rule. In this paper we generalise this literature by letting the combination weights follow more general schemes. Sieve estimation is used to optimise the score of the generalised density combination where the combination weights depend on the variable one is trying to forecast. Specific attention is paid to the use of piecewise linear weight functions that let the weights vary by region of the density. We analyse these schemes theoretically, in Monte Carlo experiments and in an empirical study. Our results show that the generalised combinations outperform their linear counterparts.
    Keywords: Density Forecasting; Model Combination; Scoring Rules
    JEL: C53
    Date: 2014–03–28
  7. By: Harin, Alexander
    Abstract: The concept of unforeseen events is considered as a part of a hypothesis of uncertain future. The applications of the consequences of the hypothesis in utility and prospect theories are reviewed. Partially unforeseen events and their role in forecasting are analyzed. Preliminary preparations are shown to be able, under specified conditions, to quicken the revisions of forecasts and to hedge or diversify financial risks after partially unforeseen events have occurred. General correcting formulae for forecasts are proposed.
    Keywords: forecast; uncertainty; risk; utility; decisions; Ellsberg paradox;
    JEL: C53 D8 D81
    Date: 2014–04–12
  8. By: Goodness C. Aye (Department of Economics, University of Pretoria); Rangan Gupta (Department of Economics, University of Pretoria); Shawkat Hammoudeh (Lebow College of Business, Drexel University, Philadelphia, USA); Won Joong Kim (Department of Economics, Konkuk University, Seoul, Korea)
    Abstract: We develop models for examining possible predictors of the return on gold that embrace six global factors (business cycle, nominal, interest rate, commodity, exchange rate and stock price factors) and two uncertainty indices (the Kansas City Fed’s financial stress index and the U.S. Economic uncertainty index). Specifically, by comparing with other alternative models, we show that the dynamic model averaging (DMA) and dynamic model selection (DMS) models outperform not only a linear model (such as random walk) but also the Bayesian model averaging (BMA) model for examining possible predictors of the return of gold. The DMS is the best overall across all forecast horizons. Generally, all the predictors show strong predictive power at one time or another though at varying magnitudes, while the exchange rate factor and the Kansas City Fed’s financial stress index appear to be strong at almost all horizons and sub-periods. However, the forecasting prowess of the exchange rate is supreme.
    Keywords: Bayesian, state space models, macroeconomic fundamentals, forecasting
    JEL: C11 C53 F37 F47 Q02
    Date: 2014–04
  9. By: Liam Cheung; John Galbraith
    Abstract: GARCH models and their variants are usually estimated using quasi-Maximum Likelihood (QML). Recent work has shown that by using estimates of quadratic variation, for example from the daily realized volatility, it is possible to estimate these models in a different way which incorporates the additional information. Theory suggests that as the precision of estimates of daily quadratic variation improves, such estimates (via LAD- ARCH approximation) should come to equal and eventually dominate the QML estimators. The present paper investigates this using a five-year sample of data on returns from all 466 S&P 500 stocks which were present in the index continuously throughout the period. The results suggest that LAD-ARCH estimates, using realized volatility on five-minute returns over the trading day, yield measures of 1-step forecast accuracy comparable or slightly superior to those obtained from QML estimates. Combining the two estimators, either by equal weighting or weighting based on cross-validation, appears to produce a clear improvement in forecast accuracy relative to either of the two different forecasting methods alone.
    Keywords: QML and LAD-ARCH estimators, GARCH models,
    Date: 2013–07–01
  10. By: Stefano Grassi (Univeristy of Kent and CREATES); Nima Nonejad (Aarhus University and CREATES); Paolo Santucci de Magistris (Aarhus University and CREATES)
    Abstract: A modification of the self-perturbed Kalman filter of Park and Jun (1992) is proposed for the on-line estimation of models subject to parameter instability. The perturbationterm in the updating equation of the state covariance matrix is weighted by the measurement error variance, thus avoiding the calibration of a design parameter. The standardization leads to a better tracking of the dynamics of the parameters compared to other on-line methods, especially as the level of noise increases. The proposed estimation method, coupled with dynamic model averaging and selection, is adopted to forecast S&P500 realized volatility series with a time-varying parameters HAR model with exogenous variables.
    Keywords: TVP models, Self-Perturbed Kalman Filter, Dynamic Model Averaging, Dynamic Model Selection, Forecasting, Realized Variance
    JEL: C10 C11 C22 C80
    Date: 2014–04–07
  11. By: Hossein Asgharian (Lund University); Charlotte Christiansen (Aarhus University and CREATES); Ai Jun Hou (Stockholm University)
    Abstract: We investigate the long-run stock-bond correlation using a novel model that combines the dynamic conditional correlation model with the mixed-data sampling approach. The long-run correlation is affected by both macro-finance variables (historical and forecasts) and the lagged realized correlation itself. Macro-finance variables and the lagged realized correlation are simultaneously significant in forecasting the long-run stock-bond correlation. The behavior of the long-run stock-bond correlation is very different when estimated taking the macro-finance variables into account. Supporting the flight-to-quality phenomenon for the total stock-bond correlation, the long-run correlation tends to be small/negative when the economy is weak.
    Keywords: DCC-MIDAS model, Long-run correlation, Macro-finance variables, Stock-bond correlation
    JEL: C32 C58 E32 E44 G11 G12
    Date: 2014–04–10
  12. By: Ching-Wai (Jeremy) Chiu (Bank of England); Haroon Mumtaz (Queen Mary University of London); Gabor Pinter (Bank of England)
    Abstract: We confirm that standard time-series models for US output growth, inflation, interest rates and stock market returns feature non-Gaussian error structure. We build a 4-variable VAR model where the orthogonolised shocks have a Student t-distribution with a time-varying variance. We find that in terms of in-sample fit, the VAR model that features both stochastic volatility and Student-t disturbances outperforms restricted alternatives that feature either attributes. The VAR model with Student-t disturbances results in density forecasts for industrial production and stock returns that are superior to alternatives that assume Gaussianity. This difference appears to be especially stark over the recent financial crisis.
    Keywords: Bayesian VAR, Fat tails, Stochastic volatility
    JEL: C32 C53
    Date: 2014–03
  13. By: Christophe Chorro (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Paris 1 - Panthéon-Sorbonne); Dominique Guegan (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Paris 1 - Panthéon-Sorbonne); Florian Ielpo (Lombard Odier - Lombard Odier Darier Hentsch & Cie); Hanjarivo Lalaharison (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Paris 1 - Panthéon-Sorbonne)
    Abstract: This article questions the empirical usefulness of leverage effects to describe the dynamics of equity returns. Using a recursive estimation scheme that accurately disentangles the asymmetry coming from the conditional distribution of returns and the asymmetry that is related to the past return to volatility component in GARCH models, we test for the statistical significance of the latter. Relying on both in and out of sample tests we consistently find a weak contribution of leverage effect over the past 25 years of S&P 500 returns, casting light on the importance of the conditional distribution in time series models.
    Keywords: Maximum likelihood method; related-GARCH process; recursive estimation method; mixture of Gaussian distributions; Generalized hyperbolic distributions; S&P 500; forecast; leverage effect
    Date: 2014–02
  14. By: Cloyne, James (Bank of England); Hürtgen, Patrick (Department of Economics, University of Bonn)
    Abstract: This paper estimates the effects of monetary policy on the UK economy based on a new, extensive real-time forecast data set. Employing the Romer–Romer identification approach we first construct a new measure of monetary policy innovations for the UK economy. We find that a 1 percentage point increase in the policy rate reduces output by up to 0.6% and inflation by up to 1.0 percentage point after two to three years. Our approach resolves the price puzzle for the United Kingdom and we show that forecasts are crucial for this result. Finally, we show that the response of policy after the initial innovation is crucial for interpreting estimates of the effect of monetary policy. We can then reconcile differences across empirical specifications, with the wider vector autoregression literature and between our United Kingdom results and the larger narrative estimates for the United States.
    Keywords: monetary policy; narrative identification; real-time forecasts; business cycles
    JEL: E31 E32 E52 E58
    Date: 2014–03–28
  15. By: Wladimir Andreff (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Paris 1 - Panthéon-Sorbonne)
    Abstract: Econometric modelling of Winter Olympic Games to explain sporting outcomes with economic variables, then predicting the medal distribution at the next Games, Sochi 2014.
    Keywords: sports economics, sporting outcome, prediction, modelling, Winter Olympic Games
    Date: 2013

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