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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.

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Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 26 (2010)
Issue (Month): 4 (October)
Pages: 777-793

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Handle: RePEc:eee:intfor:v:26:y::i:4:p:777-793
Contact details of provider: Web page: http://www.elsevier.com/locate/ijforecast

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  1. Marcelle Chauvet & Simon Potter, 2005. "Forecasting recessions using the yield curve," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(2), pages 77-103.
  2. Michael Dueker, 1998. "Conditional heteroskedasticity in qualitative response models of time series: a Gibbs sampling approach to the bank prime rate," Working Papers 1998-011, Federal Reserve Bank of St. Louis.
  3. 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.
  4. Marcelle Chauvet & Simon Potter, 2001. "Recent changes in the U.S. business cycle," Staff Reports 126, Federal Reserve Bank of New York.
  5. Margaret M. McConnell & Gabriel Perez Quiros, 1997. "Output fluctuations in the United States: what has changed since the early 1980s?," Research Paper 9735, Federal Reserve Bank of New York.
  6. John Geweke, 1998. "Using simulation methods for Bayesian econometric models: inference, development, and communication," Staff Report 249, Federal Reserve Bank of Minneapolis.
  7. Marcelle Chauvet & James D. Hamilton, 2005. "Dating Business Cycle Turning Points," NBER Working Papers 11422, National Bureau of Economic Research, Inc.
  8. 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.
  9. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1, May.
  10. 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.
  11. 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.
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