VAR Forecasting Using Bayesian Variable Selection
University of Glasgow
December 1, 2009
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.
Number of Pages in PDF File: 33
Keywords: Forecasting, variable selection, time-varying parameters, Bayesian vector autoregression
JEL Classification: C11, C32, C52, C53, E37working papers series
Date posted: March 5, 2010 ; Last revised: April 19, 2011
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