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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: http://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
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Handle: RePEc:pra:mprapa:21124
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  1. Luc Bauwens & Gary Koop & Dimitris Korobilis & Jeroen V.K. Rombouts, 2011. "A Comparison of Forecasting Procedures for Macroeconomic Series: the Contribution of Structural Break Models," Cahiers de recherche 1104, CIRPEE.
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  3. KOROBILIS, Dimitris, 2011. "Hierarchical shrinkage priors for dynamic regressions with many predictors," CORE Discussion Papers 2011021, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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  11. Gary Koop & Dimitris Korobilis, 2009. "Forecasting Inflation Using Dynamic Model Averaging," Working Paper Series 34_09, The Rimini Centre for Economic Analysis, revised Jan 2009.
  12. Korobilis, Dimitris, 2008. "Forecasting in vector autoregressions with many predictors," MPRA Paper 21122, University Library of Munich, Germany.
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  35. repec:cup:cbooks:9780521681599 is not listed on IDEAS
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