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VAR forecasting using Bayesian variable selection

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Korobilis, Dimitris [UCL]

This paper develops methods for automatic selection of variables in Bayesian vector autoregressions (VARs) using the Gibbs sampler. In particular, I provide computationally efficient algorithms for stochastic variable selection in generic linear and nonlinear models, as well as models of large dimensions. The performance of the proposed variable selection method is assessed in forecasting three major macroeconomic time series of the UK economy. Data-based restrictions of VAR coefficients can help improve upon their unrestricted counterparts in forecasting, and in many cases they compare favorably to shrinkage estimators. © 2011 John Wiley & Sons, Ltd.

Document type Article de périodique (Journal article) – Article de recherche
Access type Accès restreint
Publication date 2013
Language Anglais
Journal information "Journal of Applied Econometrics" - Vol. 28, no. 2, p. 204-230 (2013)
Peer reviewed yes
Publisher JohnWiley & Sons Ltd.
e-issn 1099-1255
issn 0883-7252
Publication status Publié
Affiliation UCL - SSH/LIDAM/CORE - Center for operations research and econometrics
Links
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Bibliographic reference Korobilis, Dimitris. VAR forecasting using Bayesian variable selection. In: Journal of Applied Econometrics, Vol. 28, no. 2, p. 204-230 (2013)
Permanent URL http://hdl.handle.net/2078.1/160544