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When did the 2001 recession really start?

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Author Info
J. Polzehl (Weierstrass Institute for Applied Analysis & Stochastics)
V. Spokoiny (Weierstrass Institute for Applied Analysis & Stochastics & Humbold University)
C. Starica (Dept. Mathematical Statistics, Chalmers University of Technology)

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Abstract

The paper develops a non-parametric, non-stationary framework for business-cycle dating based on an innovative statistical methodology known as Adaptive Weights Smoothing (AWS). The methodology is used both for the study of the individual macroeconomic time series relevant to the dating of the business cycle as well as for the estimation of their joint dynamic. Since the business cycle is defined as the common dynamic of some set of macroeconomic indicators, its estimation depends fundamentally on the group of series monitored. We apply our dating approach to two sets of US economic indicators including the monthly series of industrial production, nonfarm payroll employment, real income, wholesale-retail trade and gross domestic product (GDP). We find evidence of a change in the methodology of the NBER's Business- Cycle Dating Committee: an extended set of five monthly macroeconomic indicators replaced in the dating of the last recession the set of indicators emphasized by the NBER's Business-Cycle Dating Committee in recent decades. This change seems to seriously affect the continuity in the outcome of the dating of business cycle. Had the dating been done on the traditional set of indicators, the last recession would have lasted one year and a half longer. We find that, independent of the set of coincident indicators monitored, the last economic contraction began in November 2000, four months before the date of the NBER's Business-Cycle Dating Committee.

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Paper provided by EconWPA in its series Econometrics with number 0411017.

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Length: 39 pages
Date of creation: 22 Nov 2004
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Handle: RePEc:wpa:wuwpem:0411017

Note: Type of Document - pdf; pages: 39
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Web page: http://129.3.20.41

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Related research
Keywords: business cycle; non-parametric smoothing; non-stationarity;

Other versions of this item:

Find related papers by JEL classification:
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions

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References listed on IDEAS
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. Lawrence J. Cristiano & Terry J. Fitzgerald, 1998. "The business cycle: it's still a puzzle," Economic Perspectives, Federal Reserve Bank of Chicago, issue Q IV, pages 56-83. [Downloadable!]
  2. Sichel, Daniel E, 1994. "Inventories and the Three Phases of the Business Cycle," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 269-77, July.
  3. repec:bep:sndecm:1:1996:1:35-46 is not listed on IDEAS
  4. James H. Stock & Mark W. Watson, 1992. "A Procedure for Predicting Recessions With Leading Indicators: Econometric Issues and Recent Experience," NBER Working Papers 4014, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
    Other versions:
  5. Catalin Starica & Clive Granger, 2004. "Non-stationarities in stock returns," Econometrics 0411016, EconWPA. [Downloadable!]
    Other versions:
  6. Diebold, Francis X & Rudebusch, Glenn D, 1996. "Measuring Business Cycles: A Modern Perspective," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 67-77, February. [Downloadable!] (restricted)
    Other versions:
  7. 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)
  8. James H. Stock & Mark W. Watson, 2003. "Has the business cycle changed?," Proceedings, Federal Reserve Bank of Kansas City, pages 9-56. [Downloadable!]
  9. Catalin Starica, 2004. "Is GARCH(1,1) as good a model as the Nobel prize accolades would imply?," Econometrics 0411015, EconWPA. [Downloadable!]
  10. Otrok, Christopher & Whiteman, Charles H, 1998. "Bayesian Leading Indicators: Measuring and Predicting Economic Conditions in Iowa," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 997-1014, November.
    Other versions:
  11. 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.
  12. 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!]
    Other versions:
  13. Harding, Don & Pagan, Adrian, 2001. "Extracting, Using and Analysing Cyclical Information," MPRA Paper 15, University Library of Munich, Germany. [Downloadable!]
  14. James H. Stock & Mark W. Watson, 1988. "A Probability Model of The Coincident Economic Indicators," NBER Working Papers 2772, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
  15. 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.
    Other versions:
  16. Erica L. Groshen & Simon Potter, 2003. "Has structural change contributed to a jobless recovery?," Current Issues in Economics and Finance, Federal Reserve Bank of New York, issue Aug. [Downloadable!]
  17. Marianne Baxter & Robert G. King, 1995. "Measuring Business Cycles Approximate Band-Pass Filters for Economic Time Series," NBER Working Papers 5022, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
    Other versions:
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