Arbeitspapier
Forecasting using Bayesian and information theoretic model averaging: An application to UK inflation
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
- Language
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Englisch
- Bibliographic citation
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Series: Working Paper ; No. 566
- Classification
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Wirtschaft
Bayesian Analysis: General
Statistical Simulation Methods: General
Forecasting Models; Simulation Methods
- Subject
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Forecasting, Inflation, Bayesian model averaging, Akaike criteria, Forecast combining
- Event
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Geistige Schöpfung
- (who)
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Kapetanios, George
Labhard, Vincent
Price, Simon
- Event
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Veröffentlichung
- (who)
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Queen Mary University of London, Department of Economics
- (where)
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London
- (when)
-
2006
- Handle
- Last update
-
10.03.2025, 11:44 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Kapetanios, George
- Labhard, Vincent
- Price, Simon
- Queen Mary University of London, Department of Economics
Time of origin
- 2006
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