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

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  • George Kapetanios

    ()
    (Queen Mary, University of London)

  • Vincent Labhard

    ()
    (Bank of England)

  • Simon Price

    ()
    (Bank of England)

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.

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Paper provided by Queen Mary, University of London, School of Economics and Finance in its series Working Papers with number 566.

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Date of creation: Sep 2006
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Handle: RePEc:qmw:qmwecw:wp566

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Keywords: Forecasting; Inflation; Bayesian model averaging; Akaike criteria; Forecast combining;

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