We extend the standard approach to Bayesian forecast combination by forming the weights for the model averaged forecast from the predictive likelihood rather than the standard marginal likelihood. The use of predictive measures of fit offers greater protection against in-sample overfitting when uninformative priors on the model parameters are used and improves forecast performance. For the predictive likelihood we argue that the forecast weights have good large and small sample properties. This is confirmed in a simulation study and in an application to forecasts of the Swedish inflation rate, where forecast combination using the predictive likelihood outperforms standard Bayesian model averaging using the marginal likelihood.
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Article provided by Taylor and Francis Journals in its journal Econometric Reviews.
References listed on IDEAS Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
David F. Hendry & Michael P. Clements, 2004.
"Pooling of forecasts,"
Econometrics Journal,
Royal Economic Society, vol. 7(1), pages 1-31, 06.
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David Hendry & Michael P. Clements, 2001.
"Pooling of Forecasts,"
Economics Papers
2002-W9, Economics Group, Nuffield College, University of Oxford.
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