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

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  • Gary Koop

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

  • Markus Jochmann

    (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 structual 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. Classification-JEL:

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Bibliographic Info

Paper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 19-08.

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Date of creation: Jan 2008
Date of revision: Jan 2008
Handle: RePEc:rim:rimwps:19-08

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  2. Chib, Siddhartha, 1996. "Calculating posterior distributions and modal estimates in Markov mixture models," Journal of Econometrics, Elsevier, vol. 75(1), pages 79-97, November.
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  8. Corradi, Valentina & Swanson, Norman R., 2006. "Predictive Density Evaluation," Handbook of Economic Forecasting, Elsevier.
  9. Gary Koop & Simon M. Potter, 2009. "Prior Elicitation In Multiple Change-Point Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 50(3), pages 751-772, 08.
  10. John Geweke & Gianni Amisano, 2007. "Hierarchical Markov Normal Mixture Models with Applications to Financial Asset Returns," Working Papers 0705, University of Brescia, Department of Economics.
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  24. Chang-Jin Kim & Charles Nelson & Jeremy M. Piger, 2003. "The less volatile U.S. economy: a Bayesian investigation of timing, breadth, and potential explanations," Working Papers 2001-016, Federal Reserve Bank of St. Louis.
  25. Geweke, John & Whiteman, Charles, 2006. "Bayesian Forecasting," Handbook of Economic Forecasting, Elsevier.
  26. Martin, G.M., 1998. "U.S. Deficit Sustainability: A New Approach Based on Multiple Endogenous Breaks," Monash Econometrics and Business Statistics Working Papers 1/98, Monash University, Department of Econometrics and Business Statistics.
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