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Forecast Combination and Bayesian Model Averaging: A Prior Sensitivity Analysis

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  • Martin Feldkircher

Abstract

In this study the forecast performance of model averaged forecasts is compared to that of alternative single models. Following Eklund and Karlsson (2007) we form posterior model probabilities - the weights for the combined forecast - based on the predictive likelihood. Extending the work of Fernández et al. (2001a) we carry out a prior sensitivity analysis for a key parameter in Bayesian model averaging (BMA): Zellner's g. The main results based on a simulation study are fourfold: First the predictive likelihood does always better than the traditionally employed 'marginal' likelihood in settings where the true model is not part of the model space. Secondly, and more striking, forecast accuracy as measured by the root mean square error (rmse) is maximized for the median probability model put forward by Barbieri and Berger (2003). On the other hand, model averaging excels in predicting direction of changes, a finding that is in line with Crespo Cuaresma (2007). Lastly, our recommendation concerning the prior on g is to choose the prior proposed by Laud and Ibrahim (1995) with a hold-out sample size of 25% to minimize the rmse (median model) and 75% to optimize direction of change forecasts (model averaging). We finally forecast the monthly industrial production output of six Central Eastern and South Eastern European (CESEE) economies for a one step ahead forecasting horizon. Following the aforementioned forecasting recommendations improves the out-of-sample statistics over a 30-period horizon beating for almost all countries the first order autoregressive benchmark model.

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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 31 (2012)
Issue (Month): 4 (07)
Pages: 361-376

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Handle: RePEc:wly:jforec:v:31:y:2012:i:4:p:361-376

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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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  1. Gernot Doppelhofer & Ronald I. Miller & Xavier Sala-i-Martin, 2000. "Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach," NBER Working Papers 7750, National Bureau of Economic Research, Inc.
  2. Carmen Fernandez & Eduardo Ley & Mark Steel, 2001. "Model uncertainty in cross-country growth regressions," Econometrics 0110002, EconWPA.
  3. Jesus Crespo Cuaresma, . "Forecasting euro exchange rates: How much does model averaging help?," Working Papers 2007-24, Faculty of Economics and Statistics, University of Innsbruck.
  4. Fernandez, Carmen & Ley, Eduardo & Steel, Mark F. J., 2001. "Benchmark priors for Bayesian model averaging," Journal of Econometrics, Elsevier, vol. 100(2), pages 381-427, February.
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Cited by:
  1. Riane de Bruyn & Rangan Gupta & Renee van Eyden, 2013. "Forecasting The Rand-Dollar And Rand-Pound Exchange Rates Using Dynamic Model Averaging," Working Papers 201307, University of Pretoria, Department of Economics.
  2. Roman Horvath, 2012. "Do Confidence Indicators Help Predict Economic Activity? The Case of the Czech Republic," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 62(5), pages 398-412, November.
  3. Jesus Crespo Cuaresma & Mauro Costantini & Jaroslava Hlouskova, 2014. "Can Macroeconomists Get Rich Forecasting Exchange Rates?," Department of Economics Working Papers wuwp176, Vienna University of Economics, Department of Economics.

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