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Forecast combination and model averaging using predictive measures
Bank of England.
Örebro University, Department of Business, Economics, Statistics and Informatics. (Statistics)ORCID iD: 0000-0003-0203-4688
2007 (English)In: Econometric Reviews, ISSN 0747-4938, E-ISSN 1532-4168, Vol. 26, no 2-4, 329-363 p.Article in journal (Refereed) Published
Abstract [en]

We extend the standard approach to Bayesian forecast combination by forming the weights for the model averaged forecast from the predictive likelihood rather than the standard marginal likelihood. The use of predictive measures of fit offers greater protection against in-sample overfitting when uninformative priors on the model parameters are used and improves forecast performance. For the predictive likelihood we argue that the forecast weights have good large and small sample properties. This is confirmed in a simulation study and in an application to forecasts of the Swedish inflation rate, where forecast combination using the predictive likelihood outperforms standard Bayesian model averaging using the marginal likelihood.

Place, publisher, year, edition, pages
2007. Vol. 26, no 2-4, 329-363 p.
Keyword [en]
Bayesian model averaging; Inflation rate; Partial Bayes factor; Predictive likelihood; Training sample; Uninformative priors, Econometrics
National Category
Economics Probability Theory and Statistics
Research subject
Statistics; Economics
Identifiers
URN: urn:nbn:se:oru:diva-5673DOI: 10.1080/07474930701220550OAI: oai:DiVA.org:oru-5673DiVA: diva2:173830
Available from: 2009-02-17 Created: 2009-02-17 Last updated: 2016-08-10Bibliographically approved

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Karlsson, Sune
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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf