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

George Kapetanios, Vincent Labhard () and Simon Price

Bank of England working papers from Bank of England

Abstract: In recent years there has been increasing interest in forecasting methods that utilise large data sets, driven partly by the recognition that policymaking institutions need to process large quantities of information. Factor analysis is a popular way of doing this. Forecast combination is another, and it is on this that we concentrate. Bayesian model averaging methods have been widely employed in this area, but a neglected alternative approach employed in this paper uses information theoretic based weights. We consider the use of model averaging in forecasting UK inflation with a large data set 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.

Date: 2005-07
New Economics Papers: this item is included in nep-cba, nep-ecm, nep-ets, nep-for and nep-ict
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Citations: View citations in EconPapers (17)

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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 (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:boe:boeewp:268

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