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

  • 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)

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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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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  1. John M. Maheu & Stephen Gordon, 2004. "Learning, Forecasting and Structural Breaks," Cahiers de recherche 0422, CIRPEE.
  2. Valentina Corradi & Norman Swanson, 2004. "Predictive Density Evaluation," Departmental Working Papers 200419, Rutgers University, Department of Economics.
  3. Luboš Pástor & Robert F. Stambaugh, 2000. "The Equity Premium and Structural Breaks," CRSP working papers 519, Center for Research in Security Prices, Graduate School of Business, University of Chicago.
  4. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
  5. M. Hashem Pesaran & Davide Pettenuzzo & Allan Timmermann, 2006. "Forecasting Time Series Subject to Multiple Structural Breaks," Review of Economic Studies, Oxford University Press, vol. 73(4), pages 1057-1084.
  6. Gary Koop & Simon Potter, 2004. "Forecasting in dynamic factor models using Bayesian model averaging," Econometrics Journal, Royal Economic Society, vol. 7(2), pages 550-565, December.
  7. 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.
  8. Andersson, Michael K & Karlsson, Sune, 2007. "Bayesian Forecast Combination for VAR Models," Working Papers 2007:13, Örebro University, School of Business.
  9. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," Review of Economic Studies, Oxford University Press, vol. 72(3), pages 821-852.
  10. Chib, Siddhartha, 1996. "Calculating posterior distributions and modal estimates in Markov mixture models," Journal of Econometrics, Elsevier, vol. 75(1), pages 79-97, November.
  11. Stock, James H & Watson, Mark W, 1996. "Evidence on Structural Instability in Macroeconomic Time Series Relations," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 11-30, January.
  12. Gary Koop & Simon M. Potter, 1999. "Are apparent findings of nonlinearity due to structural instability in economic time series?," Staff Reports 59, Federal Reserve Bank of New York.
  13. Gary M. Koop & Simon M. Potter, 2004. "Prior elicitation in multiple change-point models," Staff Reports 197, Federal Reserve Bank of New York.
  14. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
  15. Maheu, John M. & McCurdy, Thomas H., 2009. "How Useful are Historical Data for Forecasting the Long-Run Equity Return Distribution?," Journal of Business & Economic Statistics, American Statistical Association, vol. 27, pages 95-112.
  16. Kim, Chang-Jin & Nelson, Charles R & Piger, Jeremy, 2004. "The Less-Volatile U.S. Economy: A Bayesian Investigation of Timing, Breadth, and Potential Explanations," Journal of Business & Economic Statistics, American Statistical Association, vol. 22(1), pages 80-93, January.
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  20. Massimiliano Marcellino & James Stock & Mark Watson, 2005. "A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series," Working Papers 285, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  21. Geweke, John & Whiteman, Charles, 2006. "Bayesian Forecasting," Handbook of Economic Forecasting, Elsevier.
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  25. 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.
  26. Ang, Andrew & Bekaert, Geert, 2002. "Regime Switches in Interest Rates," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 163-82, April.
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  28. Valentina Corradi & Norman Swanson, 2006. "Predictive Density Evaluation. Revised," Departmental Working Papers 200621, Rutgers University, Department of Economics.
  29. repec:cup:cbooks:9780521632423 is not listed on IDEAS
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