Forecast Combination and Model Averaging Using Predictive Measures

Jana Eklund and Sune Karlsson ()

No 5268, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: 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 and improves forecast performance. For the predictive likelihood we show analytically that the forecast weights have good large and small sample properties. This is confirmed in a simulation study and 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.

Keywords: Bayesian model averaging; Predictive likelihood; Partial bayes factor; Training sample; Inflation rate (search for similar items in EconPapers)
JEL-codes: C11 C51 C52 C53 (search for similar items in EconPapers)
Date: 2005-10
New Economics Papers: this item is included in nep-ets and nep-for
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Citations: View citations in EconPapers (21)

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Related works:
Journal Article: Forecast Combination and Model Averaging Using Predictive Measures (2007)
Working Paper: Forecast Combination and Model Averaging using Predictive Measures (2005)
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