Forecasting using Bayesian and information theoretic model averaging: an application to UK in flation

George Kapetanios (), Vincent Labhard () and Simon Price ()
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George Kapetanios: Queen Mary University of London

No 07/15, City University Economics Discussion Papers from Department of Economics, City University, London

Abstract: Model averaging often improves forecast accuracy over individual forecasts. It may also be seen as a means of forecasting in data-rich environments. Bayesian model averaging methods have been widely advocated, but a neglected frequentist approach is to use information theoretic based weights. We consider the use of information-theoretic model averaging in forecasting UK inflation, with a large data set, and find that it can be a powerful alternative to Bayesian averaging schemes.

Keywords: forecasting; inflation; Bayesian model averaging; Akaike criteria; forecast combining. (search for similar items in EconPapers)
JEL-codes: C11 C15 C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cba, nep-ets and nep-for
Date: 2007-11
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Related works:
Working Paper: Forecasting using Bayesian and information theoretic model averaging: an application to UK inflation
Working Paper: Forecasting using Bayesian and Information Theoretic Model Averaging: An Application to UK Inflation (2006)
Journal Article: Forecasting Using Bayesian and Information-Theoretic Model Averaging: An Application to U.K. Inflation (2008)
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Persistent link: http://EconPapers.repec.org/RePEc:cty:dpaper:0715

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