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Bayesian forecasting using reduced rank VARs
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]

Reduced rank regression has a long tradition as a technique to achieve a parsimonious parameterization in multivariate regression models. Recently this has been applied in the Bayesian VAR framework where the rich parameterization is a common concern in applied work. We advocate a parameterization of the reduced rank VAR which leads to a natural interpretation in terms of a dynamic factor model. Without additional restrictions on the parameters the reduced rank model is unidentified and we consider two identification schemes. The traditional ad-hoc identification with the first rows of one of the reduced rank parameter matrices being the identity matrix and a semi-orthogonal identification originally proposed in the context of cointegrated VAR models with the advantage that it does not depend on the ordering of the variables. Borrowing from the cointegration literature, we propose efficient MCMC algorithms for the evaluation of the posterior distribution given the two identification schemes. The determination of the rank of the reduced rank VAR is an important practical issue and we study the performance of different criteria for determining the rank. Finally, the forecasting performance of the reduced rank VAR model is evaluated in comparison with other popular forecasting models for large data sets.

Keyword [en]
Model selection, Bayesian VAR model, Reduced rank regression, Markov chain Monte Carlo
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:oru:diva-35875OAI: oai:DiVA.org:oru-35875DiVA: diva2:736590
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

Direct link
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