Forecast Combination and Bayesian Model Averaging - A Prior Sensitivity Analysis
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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- Carmen Fernandez & Eduardo Ley & Mark F.J. Steel, 1998.
"Benchmark Priors for Bayesian Model Averaging,"
9804001, EconWPA, revised 31 Jul 1999.
- Carmen Fernández & Eduardo Ley & Mark F. J. Steel, "undated". "Benchmark priors for Bayesian Model averaging," Working Papers 98-06, FEDEA.
- Carmen Fernandez & Eduardo Ley & Mark F J Steel, 1998. "Benchmark priors for Bayesian model averaging," ESE Discussion Papers 26, Edinburgh School of Economics, University of Edinburgh.
- Carmen Fernandez & Eduardo Ley & Mark F J Steel, 1998. "Benchmark priors for Bayesian model averaging," ESE Discussion Papers 66, Edinburgh School of Economics, University of Edinburgh.
- Carmen Fernandez & Eduardo Ley & Mark Steel, 1999.
"Model uncertainty in cross-country growth regressions,"
9903003, EconWPA, revised 06 Oct 2001.
- Carmen Fernandez & Eduardo Ley & Mark F. J. Steel, 2001. "Model uncertainty in cross-country growth regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(5), pages 563-576.
- Carmen Fernandez & Eduardo Ley & Mark Steel, 2001. "Model uncertainty in cross-country growth regressions," Econometrics 0110002, EconWPA.
- 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.
- Xavier Sala-I-Martin & Gernot Doppelhofer & Ronald I. Miller, 2004. "Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach," American Economic Review, American Economic Association, vol. 94(4), pages 813-835, September.
- Gernot Doppelhofer & Ronald I. Miller & Xavier Sala-i-Martin, 2000. "Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (Bace) Approach," OECD Economics Department Working Papers 266, OECD Publishing.
- Jesus Crespo Cuaresma, "undated". "Forecasting euro exchange rates: How much does model averaging help?," Working Papers 2007-24, Faculty of Economics and Statistics, University of Innsbruck.
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