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

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  • Kapetanios, G.
  • Labhard, V.
  • Price, S.

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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Bibliographic Info

Paper provided by Department of Economics, City University London in its series Working Papers with number 07/15.

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Date of creation: 2007
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Handle: RePEc:cty:dpaper:07/15

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Postal: Department of Economics, Social Sciences Building, City University London, Whiskin Street, London, EC1R 0JD, United Kingdom,
Phone: +44 (0)20 7040 8500
Web page: http://www.city.ac.uk
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