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

Markus Jochmann, Gary Koop, Rodney W. Strachan

Research output: Contribution to journalArticle

18 Citations (Scopus)

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 VAR to be set to zero. The second extension allows for an unknown number of structural breaks in the VAR 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.
Original languageEnglish
Pages (from-to)326-347
Number of pages21
JournalInternational Journal of Forecasting
Volume26
Issue number2
DOIs
Publication statusPublished - Apr 2010

Keywords

  • vector autoregressive model
  • predictive density
  • over-parameterization
  • structural break
  • shrinkage

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  • Research Output

    • 18 Citations
    • 1 Working paper

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

    Jochmann, M., Koop, G. & Strachan, R. W., Jun 2008, (Unpublished) Glasgow: University of Strathclyde, 34 p.

    Research output: Working paper

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