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Business cycle monitoring with structural changes


  • Chauvet, Marcelle
  • Potter, Simon


This paper examines the predictive content of coincident variables for monitoring US recessions in the presence of instabilities. We propose several specifications of the probit model for classifying phases of the business cycle. We find strong evidence in favor of those that allow for the possibility that the economy has experienced recurrent breaks. The recession probabilities of these models provide a clearer classification of the business cycle into expansion and recession periods, and superior performance in the ability to correctly call recessions and avoid false recession signals. Overall, the sensitivity, specificity, and accuracy of these models are far superior, as is their ability to signal recessions in a timely fashion. The results indicate the importance of considering recurrent breaks for monitoring business cycles.

Suggested Citation

  • Chauvet, Marcelle & Potter, Simon, 2010. "Business cycle monitoring with structural changes," International Journal of Forecasting, Elsevier, vol. 26(4), pages 777-793, October.
  • Handle: RePEc:eee:intfor:v:26:y::i:4:p:777-793

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    References listed on IDEAS

    1. Marianne Sensier & Dick van Dijk, 2004. "Testing for Volatility Changes in U.S. Macroeconomic Time Series," The Review of Economics and Statistics, MIT Press, vol. 86(3), pages 833-839, August.
    2. Chib, Siddhartha, 2001. "Markov chain Monte Carlo methods: computation and inference," Handbook of Econometrics,in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 57, pages 3569-3649 Elsevier.
    3. Marcelle Chauvet & Simon Potter, 2005. "Forecasting recessions using the yield curve," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(2), pages 77-103.
    4. Michael Dueker, 2005. "Dynamic Forecasts of Qualitative Variables: A Qual VAR Model of U.S. Recessions," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 96-104, January.
    5. 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.
    6. John Geweke, 1999. "Using simulation methods for bayesian econometric models: inference, development,and communication," Econometric Reviews, Taylor & Francis Journals, vol. 18(1), pages 1-73.
    7. Margaret M. McConnell & Gabriel Perez-Quiros, 2000. "Output fluctuations in the United States: what has changed since the early 1980s?," Proceedings, Federal Reserve Bank of San Francisco, issue Mar.
    8. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1, January.
    9. Chauvet, Marcelle & Potter, Simon, 2001. "Recent Changes in the US Business Cycle," Manchester School, University of Manchester, vol. 69(5), pages 481-508, Special I.
    10. Marcelle Chauvet & James D. Hamilton, 2005. "Dating Business Cycle Turning Points," NBER Working Papers 11422, National Bureau of Economic Research, Inc.
    11. Dueker, Michael, 1999. "Conditional Heteroscedasticity in Qualitative Response Models of Time Series: A Gibbs-Sampling Approach to the Bank Prime Rate," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(4), pages 466-472, October.
    12. Chauvet, Marcelle & Potter, Simon, 2002. "Predicting a recession: evidence from the yield curve in the presence of structural breaks," Economics Letters, Elsevier, vol. 77(2), pages 245-253, October.
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    Cited by:

    1. Jiang, Yu & Song, Zhe & Kusiak, Andrew, 2013. "Very short-term wind speed forecasting with Bayesian structural break model," Renewable Energy, Elsevier, vol. 50(C), pages 637-647.
    2. Huang, MeiChi, 2014. "Bubble-like housing boom–bust cycles: Evidence from the predictive power of households’ expectations," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(1), pages 2-16.
    3. repec:sbe:breart:v:31:y:2011:i:1:a:3630 is not listed on IDEAS
    4. Chan, Felix & Pauwels, Laurent L. & Wongsosaputro, Johnathan, 2013. "The impact of serial correlation on testing for structural change in binary choice model: Monte Carlo evidence," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 93(C), pages 175-189.


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