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VAR forecasting using Bayesian variable selection

  • KOROBILIS, Dimitris

    ()

    (Université catholique de Louvain, CORE, B-1348 Louvain-la-Neuve, Belgium)

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. Databased restrictions of VAR coefficients can help improve upon their unrestricted counterparts in forecasting, and in many cases they compare favorably to shrinkage estimators.

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Paper provided by Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) in its series CORE Discussion Papers with number 2011022.

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Date of creation: 01 May 2011
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Handle: RePEc:cor:louvco:2011022
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