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Erscheinungsjahr: 
2006
Schriftenreihe/Nr.: 
Working Paper No. 566
Verlag: 
Queen Mary University of London, Department of Economics, London
Zusammenfassung: 
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
Schlagwörter: 
Forecasting, Inflation, Bayesian model averaging, Akaike criteria, Forecast combining
JEL: 
C11
C15
C53
Dokumentart: 
Working Paper

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