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VAR forecasting using Bayesian variable selection

Dimitris Korobilis

MPRA Paper from University Library of Munich, Germany

Abstract: 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.

Keywords: Forecasting; variable selection; time-varying parameters; Bayesian (search for similar items in EconPapers)
JEL-codes: C32 C53 C52 E37 C11 E47 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-ore
Date: 2009-12
References: View references in EconPapers View complete reference list from CitEc
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https://mpra.ub.uni-muenchen.de/21124/1/MPRA_paper_21124.pdf original version (application/pdf)

Related works:
Journal Article: VAR FORECASTING USING BAYESIAN VARIABLE SELECTION (2013)
Working Paper: VAR forecasting using Bayesian variable selection (2011) Downloads
Working Paper: VAR Forecasting Using Bayesian Variable Selection (2011) Downloads
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