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

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Author Info
Marcelle Chauvet
Jeremy M. Piger

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Abstract

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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Keywords: Business cycles;

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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. 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. [Downloadable!]
  2. Harding, Don & Pagan, Adrian, 2006. "Synchronization of cycles," Journal of Econometrics, Elsevier, vol. 132(1), pages 59-79, May. [Downloadable!] (restricted)
  3. 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. [Downloadable!] (restricted)
  4. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, vol. 105(1), pages 111-130, November. [Downloadable!] (restricted)
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  5. Kim, Chang-Jin, 1994. "Dynamic linear models with Markov-switching," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 1-22. [Downloadable!] (restricted)
  6. 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.
  7. James H. Stock & Mark W. Watson, 1989. "New Indexes of Coincident and Leading Economic Indicators," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 351-409 National Bureau of Economic Research, Inc. [Downloadable!]
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  8. 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. [Downloadable!] (restricted)
  9. 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. [Downloadable!]
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(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Michael T. Owyang & Jeremy M. Piger & Howard J. Wall, 2005. "The 2001 recession and the states of the Eighth Federal Reserve District," Regional Economic Development, Federal Reserve Bank of St. Louis, issue Nov, pages 3-16. [Downloadable!]
    Other versions:
  2. Vasyl Golosnoy & Jens Hogrefe, 2009. "Sequential Methodology for Signaling Business Cycle Turning Points," Kiel Working Papers 1528, Kiel Institute for the World Economy. [Downloadable!]
  3. Sylvia Kaufmann, 2008. "Dating and forecasting turning points by Bayesian clustering with dynamic structure: A suggestion with an application to Austrian data," Working Papers 144, Oesterreichische Nationalbank (Austrian Central Bank). [Downloadable!]
  4. Dean Croushore, 2008. "Frontiers of real-time data analysis," Working Papers 08-4, Federal Reserve Bank of Philadelphia. [Downloadable!]
  5. David N. DeJong & Hariharan Dharmarajan & Roman Liesenfeld & Jean-Francois Richard, 2008. "Exploiting Non-Linearities in GDP Growth for Forecasting and Anticipating Regime Changes," Working Papers 367, University of Pittsburgh, Department of Economics, revised Sep 2008. [Downloadable!]
  6. Heikki Kauppi, 2008. "Yield-Curve Based Probit Models for Forecasting U.S. Recessions: Stability and Dynamics," Discussion Papers 31, Aboa Centre for Economics. [Downloadable!]
  7. Troy Davig, 2008. "Detecting recessions in the Great Moderation: a real-time analysis," Economic Review, Federal Reserve Bank of Kansas City, issue Q IV, pages 5-33. [Downloadable!]
  8. Darné, O. & Ferrara, L., 2009. "Identification of slowdowns and accelerations for the euro area economy," Documents de Travail 239, Banque de France. [Downloadable!]
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