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VAR forecasting using Bayesian variable selection

  • Korobilis, Dimitris

This paper develops methods for automatic selection of variables in forecasting Bayesian vector autoregressions (VARs) using the Gibbs sampler. In particular, I provide computationally efficient algorithms for stochastic variable selection in generic (linear and nonlinear) VARs. The performance of the proposed variable selection method is assessed in a small Monte Carlo experiment, and in forecasting 4 macroeconomic series of the UK using time-varying parameters vector autoregressions (TVP-VARs). Restricted models consistently improve upon their unrestricted counterparts in forecasting, showing the merits of variable selection in selecting parsimonious models.

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File URL: https://mpra.ub.uni-muenchen.de/21124/1/MPRA_paper_21124.pdf
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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 21124.

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Date of creation: Dec 2009
Handle: RePEc:pra:mprapa:21124
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