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VAR Forecasting Using Bayesian Variable Selection

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  • Dimitris Korobilis

    (Université Catholique de Louvain; The Rimini Centre for Economic Analysis (RCEA))

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.

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Bibliographic Info

Paper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 51_10.

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Date of creation: Jan 2010
Date of revision: Apr 2011
Handle: RePEc:rim:rimwps:51_10

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Keywords: Forecasting; variable selection; time-varying parameters; Bayesian vector autoregression;

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References

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  7. Anthony Garratt & Gary Koop & Emi Mise & Shaun Vahey, 2008. "Real-time Prediction with UK Monetary Aggregates in the Presence of Model Uncertainty," Reserve Bank of New Zealand Discussion Paper Series DP2008/13, Reserve Bank of New Zealand.
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  29. Korobilis, Dimitris, 2008. "Forecasting in vector autoregressions with many predictors," MPRA Paper 21122, University Library of Munich, Germany.
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