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VAR Forecasting Using Bayesian Variable Selection

Dimitris Korobilis

Working Paper series from Rimini Centre for Economic Analysis

Abstract: 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.

Keywords: Forecasting; variable selection; time-varying parameters; Bayesian vector autoregression (search for similar items in EconPapers)
JEL-codes: C11 C32 C52 C53 E37 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ets, nep-for and nep-ore
Date: 2010-01, Revised 2011-04
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11) Track citations by RSS feed

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http://www.rcea.org/RePEc/pdf/wp51_10.pdf (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 (2009) Downloads
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