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

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Author Info

  • 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

Related research

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

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References

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Citations

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Cited by:
  1. SENBETA, Sisay Regassa, 2012. "How important are external shocks in explaining growth in Sub-Saharan Africa? Evidence from a Bayesian VAR," Working Papers 2012010, University of Antwerp, Faculty of Applied Economics.
  2. KOROBILIS, Dimitris, 2011. "VAR forecasting using Bayesian variable selection," CORE Discussion Papers 2011022, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  3. Koop, Gary & Korobilis, Dimitris, 2013. "Large time-varying parameter VARs," Journal of Econometrics, Elsevier, vol. 177(2), pages 185-198.
  4. Gupta, Rangan & Steinbach, Rudi, 2013. "A DSGE-VAR model for forecasting key South African macroeconomic variables," Economic Modelling, Elsevier, vol. 33(C), pages 19-33.
  5. Koop, Gary, 2011. "Forecasting with Medium and Large Bayesian VARs," SIRE Discussion Papers 2011-38, Scottish Institute for Research in Economics (SIRE).
  6. Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2011. "Combination Schemes for Turning Point Predictions," Tinbergen Institute Discussion Papers 11-123/4, Tinbergen Institute.
  7. Dimitris, Korobilis, 2013. "Forecasting with Factor Models: A Bayesian Model Averaging Perspective," MPRA Paper 52724, University Library of Munich, Germany.
  8. Knut Are Aastveit & Anne Sofie Jore & Francesco Ravazzolo, 2014. "Forecasting recessions in real time," Working Paper 2014/02, Norges Bank.
  9. Nikolaus Hautsch & Dieter Hess & Fuyu Yang, 2014. "Bayesian Stochastic Search for the Best Predictors: Nowcasting GDP Growth," University of East Anglia Applied and Financial Economics Working Paper Series 056, School of Economics, University of East Anglia, Norwich, UK..
  10. Karlsson, Sune, 2012. "Forecasting with Bayesian Vector Autoregressions," Working Papers 2012:12, Örebro University, School of Business.
  11. Rangan Gupta & Rudi Steinbach, 2010. "Forecasting Key Macroeconomic Variables of the South African Economy: A Small Open Economy New Keynesian DSGE-VAR Model," Working Papers 201019, University of Pretoria, Department of Economics.

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