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Bayesian forecasting combination in VAR models with many predictors
Örebro University, Örebro University School of Business.
Örebro University, Örebro University School of Business.ORCID iD: 0000-0003-0203-4688
(English)Manuscript (preprint) (Other academic)
Abstract [en]

This paper is motivated by the findings of our previous work, that is forecasting VAR models in the cases of small and medium-sized datasets, both marginalized marginal likelihood and predictive likelihood based averaging approaches tend to produce superior forecasts than the Bayesian VAR methods using shrinkage priors. With an efficient reversible-jump MCMC algorithm, We extend the forecast combination and model averaging of VAR models to the context of large datasets (more than hundred predictors), and consider a range of competitive alternative methods to compare and examine their forecast performance. Our empirical results show that the Bayesian model averaging approach outperforms the various alternatives.

Keyword [en]
Bayesian model averaging, large datasets, marginalized marginal likelihood, reversible-jump MCMC
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:oru:diva-35874OAI: oai:DiVA.org:oru-35874DiVA: diva2:736588
Available from: 2014-08-07 Created: 2014-08-07 Last updated: 2017-10-17Bibliographically approved
In thesis
1. Model choice in Bayesian VAR models
Open this publication in new window or tab >>Model choice in Bayesian VAR models
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Place, publisher, year, edition, pages
Örebro: Örebro university, 2014
Series
Örebro Studies in Statistics, ISSN 1651-8608
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
urn:nbn:se:oru:diva-34612 (URN)
Public defence
2014-06-02, Forumhuset, Biografen, Örebro universitet, Fakultetsgatan 1, Örebro, 13:15 (English)
Opponent
Available from: 2014-04-08 Created: 2014-04-08 Last updated: 2017-10-17Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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