Forecasting using Bayesian and Information Theoretic Model Averaging: An Application to UK Inflation

George Kapetanios, Vincent Labhard () and Simon Price

No 566, Working Papers from Queen Mary University of London, School of Economics and Finance

Abstract: In recent years there has been increasing interest in forecasting methods that utilise large datasets, driven partly by the recognition that policymaking institutions need to process large quantities of information. Factor analysis is one popular way of doing this. Forecast combination is another, and it is on this that we concentrate. Bayesian model averaging methods have been widely advocated in this area, but a neglected frequentist approach is to use information theoretic based weights. We consider the use of model averaging in forecasting UK inflation with a large dataset from this perspective. We find that an information theoretic model averaging scheme can be a powerful alternative both to the more widely used Bayesian model averaging scheme and to factor models.

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)
Date: 2006-09-01
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Related works:
Journal Article: Forecasting Using Bayesian and Information-Theoretic Model Averaging: An Application to U.K. Inflation (2008)
Working Paper: Forecasting using Bayesian and information theoretic model averaging: an application to UK inflation (2007)
Working Paper: Forecasting using Bayesian and information theoretic model averaging: an application to UK inflation (2005)
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Persistent link: https://EconPapers.repec.org/RePEc:qmw:qmwecw:566

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