VAR forecasting using Bayesian variable selection
MPRA Paper from University Library of Munich, Germany
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
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Journal Article: VAR FORECASTING USING BAYESIAN VARIABLE SELECTION (2013)
Working Paper: VAR forecasting using Bayesian variable selection (2011)
Working Paper: VAR Forecasting Using Bayesian Variable Selection (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:21124
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