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

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

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 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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  11. Wright, Jonathan H., 2008. "Bayesian Model Averaging and exchange rate forecasts," Journal of Econometrics, Elsevier, vol. 146(2), pages 329-341, October.
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  13. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
  14. Eklund, Jana & Karlsson, Sune, 2005. "Forecast Combination and Model Averaging Using Predictive Measures," CEPR Discussion Papers 5268, C.E.P.R. Discussion Papers.
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  18. George Kapetanios, 2005. "Variable Selection using Non-Standard Optimisation of Information Criteria," Working Papers 533, Queen Mary University of London, School of Economics and Finance.
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