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Bayesian forecasting using stochastic search variable selection in a VAR subject to breaks

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  • Jochmann, Markus
  • Koop, Gary
  • Strachan, Rodney W.

Abstract

This paper builds a model which has two extensions over a standard VAR. The first of these is stochastic search variable selection, which is an automatic model selection device that allows coefficients in a possibly over-parameterized VARÂ to be set to zero. The second extension allows for an unknown number of structural breaks in the VARÂ parameters. We investigate the in-sample and forecasting performance of our model in an application involving a commonly-used US macroeconomic data set. In a recursive forecasting exercise, we find moderate improvements over a standard VAR, although most of these improvements are due to the use of stochastic search variable selection rather than to the inclusion of breaks.

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Bibliographic Info

Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 26 (2010)
Issue (Month): 2 (April)
Pages: 326-347

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Handle: RePEc:eee:intfor:v:26:y::i:2:p:326-347

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Web page: http://www.elsevier.com/locate/ijforecast

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Keywords: Vector autoregressive model Predictive density Over-parameterization Structural break Shrinkage;

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References

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