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A comparison of the real-time performance of business cycle dating methods

  • Marcelle Chauvet
  • Jeremy M. Piger

This paper evaluates the ability of formal rules to establish U.S. business cycle turning point dates in real time. We consider two approaches, a nonparametric algorithm and a parametric Markov-switching dynamic-factor model. In order to accurately assess the real-time performance of these rules, we construct a new unrevised "real-time" data set of employment, industrial production, manufacturing and trade sales, and personal income. We then apply the rules to this data set to simulate the accuracy and timeliness with which they would have identified the NBER business cycle chronology had they been used in real time for the past 30 years. Both approaches accurately identified the NBER dated turning points in the sample in real time, with no instances of false positives. Further, both approaches, and especially the Markov-switching model, yielded significant improvement over the NBER in the speed with which business cycle troughs were identified. In addition to suggesting that business cycle dating rules are an informative tool to use alongside the traditional NBER analysis, these results provide formal evidence regarding the speed with which macroeconomic data reveals information about new business cycle phases.

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Paper provided by Federal Reserve Bank of St. Louis in its series Working Papers with number 2005-021.

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Date of creation: 2005
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Handle: RePEc:fip:fedlwp:2005-021
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  1. Gerhard Bry & Charlotte Boschan, 1971. "Cyclical Analysis of Time Series: Selected Procedures and Computer Programs," NBER Books, National Bureau of Economic Research, Inc, number bry_71-1, June.
  2. Chauvet, Marcelle, 1998. "An Econometric Characterization of Business Cycle Dynamics with Factor Structure and Regime Switching," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 969-96, November.
  3. Harding, Don & Pagan, Adrian, 2006. "Synchronization of cycles," Journal of Econometrics, Elsevier, vol. 132(1), pages 59-79, May.
  4. Harding, Don & Pagan, Adrian, 2003. "A comparison of two business cycle dating methods," Journal of Economic Dynamics and Control, Elsevier, vol. 27(9), pages 1681-1690, July.
  5. Kim, C-J., 1991. "Dynamic Linear Models with Markov-Switching," Papers 91-8, York (Canada) - Department of Economics.
  6. Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.
  7. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, vol. 105(1), pages 111-130, November.
  8. Marcelle Chauvet & Jeremy M. Piger, 2003. "Identifying business cycle turning points in real time," Review, Federal Reserve Bank of St. Louis, issue Mar, pages 47-61.
  9. Stock, J.H. & Watson, M.W., 1989. "New Indexes Of Coincident And Leading Economic Indicators," Papers 178d, Harvard - J.F. Kennedy School of Government.
  10. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March.
  11. Layton, Allan P., 1996. "Dating and predicting phase changes in the U.S. business cycle," International Journal of Forecasting, Elsevier, vol. 12(3), pages 417-428, September.
  12. Ernst. A. Boehm & Geoffrey H. Moore, 1984. "New Economic Indicators for Australia, 1949-84," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 17(4), pages 34-56.
  13. Chang-Jin Kim & Charles R. Nelson, 1998. "Business Cycle Turning Points, A New Coincident Index, And Tests Of Duration Dependence Based On A Dynamic Factor Model With Regime Switching," The Review of Economics and Statistics, MIT Press, vol. 80(2), pages 188-201, May.
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