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Bayesian VAR models with asymmetric lags
Örebro University, Örebro University School of Business.
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Most studies estimate the VAR models with equal lag length. Little attention has been paid to the issue of lag specifications. In this paper we propose VAR models with asymmetric lags via Bayesian sparse learning. Three popular sparse priors, L1-penalized Lasso, the mixture of L1 and L2 penalties elastic net, and spike and slab type are developed using hierarchical Bayes formulation. The model identification performance is assessed with Monte Carlo experiment and the forecasting performance is evaluated with US macroeconomic data.

Keyword [en]
Bayesian shrinkage, vector autoregression, sparsity, Lasso, elastic net, spike and slab prior, asymmetric lags
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:oru:diva-35876OAI: oai:DiVA.org:oru-35876DiVA: diva2:736591
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
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Citation style
  • apa
  • ieee
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  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
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  • text
  • asciidoc
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