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

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

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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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
Contact details of provider: Web page: http://www.elsevier.com/locate/ijforecast

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  1. John M. Maheu & Stephen Gordon, 2004. "Learning, Forecasting and Structural Breaks," Cahiers de recherche 0422, CIRPEE.
  2. Korobilis, Dimitris, 2008. "Forecasting in vector autoregressions with many predictors," MPRA Paper 21122, University Library of Munich, Germany.
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  13. Gary Koop & Simon M. Potter, 2007. "Prior Elicitation in Multiple Change-point Models," Working Paper Series 17-07, The Rimini Centre for Economic Analysis, revised Jul 2007.
  14. Gary Koop & Simon M. Potter, 2007. "Estimation and Forecasting in Models with Multiple Breaks," Review of Economic Studies, Oxford University Press, vol. 74(3), pages 763-789.
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  29. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
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