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Bayesian Forecasting using Stochastic Search Variable Selection in a VAR Subject to Breaks

Author

Listed:
  • Markus Jochmann

    (University of Strathclyde, Glasgow, UK and The Rimini Centre for Economic Analysis, Italy)

  • Gary Koop

    (University of Strathclyde, Glasgow, UK and The Rimini Centre for Economic Analysis, Italy)

  • Rodney W. Strachan

    (University of Queensland, UK and The Rimini Centre for Economic Analysis, Italy)

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 which allows for coefficients in a possibly over-parameterized VAR to be set to zero. The second 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 macro-economic data set. We find that, in-sample, these extensions clearly are warranted. 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 the inclusion of breaks.

Suggested Citation

  • Markus Jochmann & Gary Koop & Rodney W. Strachan, 2008. "Bayesian Forecasting using Stochastic Search Variable Selection in a VAR Subject to Breaks," Working Paper series 19_08, Rimini Centre for Economic Analysis.
    • Handle: RePEc:rim:rimwps:19_08
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