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Forecast Combination and Model Averaging Using Predictive Measures

  • Jana Eklund
  • Sune Karlsson

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 & Francis Journals in its journal Econometric Reviews.

Volume (Year): 26 (2007)
Issue (Month): 2-4 ()
Pages: 329-363

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Handle: RePEc:taf:emetrv:v:26:y:2007:i:2-4:p:329-363
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  1. Chevillon, Guillaume & Hendry, David F., 2005. "Non-parametric direct multi-step estimation for forecasting economic processes," International Journal of Forecasting, Elsevier, vol. 21(2), pages 201-218.
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  11. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
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