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Sequential Detection of US Business Cycle Turning Points: Performances of Shiryayev-Roberts, CUSUM and EWMA Procedures

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Author Info
Bakhodir A Ergashev (Washington University)

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Abstract

In this paper we consider the problem of sequential detecting change points in economic time series. We compare the performances of three well known procedures, Shiryayev-Roberts, CUSUM and EWMA, in the problem of early detection of the US business cycle turning points using leading indicators or some financial series. The comparison was done separately for detecting recessions and expansions during the period of 1955-2003. We found that in most cases the Shiryayev-Roberts procedure is superior to the other two in detecting turning points with leading indicators. At the same time the CUSUM procedure performs better in detecting turning points with stock price indices.

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Paper provided by EconWPA in its series Econometrics with number 0402001.

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Length: 17 pages
Date of creation: 02 Feb 2004
Date of revision: 16 Mar 2004
Handle: RePEc:wpa:wuwpem:0402001

Note: Type of Document - ; prepared on WinXP; pages: 17; figures: 6
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Related research
Keywords: Business cycles; change point detection; leading indicators;

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Find related papers by JEL classification:
C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General
C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles

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  1. Jushan Bai, 1997. "Estimation Of A Change Point In Multiple Regression Models," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 551-563, November. [Downloadable!] (restricted)
  2. Neftci, Salih N, 1984. "Are Economic Time Series Asymmetric over the Business Cycle?," Journal of Political Economy, University of Chicago Press, vol. 92(2), pages 307-28, April. [Downloadable!] (restricted)
  3. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February. [Downloadable!] (restricted)
  4. James H. Stock & Mark W. Watson, 1994. "Evidence on structural instability in macroeconomic times series relations," Working Paper Series, Macroeconomic Issues 94-13, Federal Reserve Bank of Chicago.
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  5. Zivot, Eric & Andrews, Donald W K, 1992. "Further Evidence on the Great Crash, the Oil-Price Shock, and the Unit-Root Hypothesis," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(3), pages 251-70, July.
    Other versions:
  6. Chib, Siddhartha, 1998. "Estimation and comparison of multiple change-point models," Journal of Econometrics, Elsevier, vol. 86(2), pages 221-241, June. [Downloadable!] (restricted)
  7. Hansen, Bruce E., 2000. "Testing for structural change in conditional models," Journal of Econometrics, Elsevier, vol. 97(1), pages 93-115, July. [Downloadable!] (restricted)
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  8. Cooley, Thomas F & Prescott, Edward C, 1973. "An Adaptive Regression Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 14(2), pages 364-71, June. [Downloadable!] (restricted)
  9. Bruce E. Hansen, 2001. "The New Econometrics of Structural Change: Dating Breaks in U.S. Labour Productivity," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 117-128, Fall. [Downloadable!] (restricted)
  10. Cooley, Thomas F & Prescott, Edward C, 1976. "Estimation in the Presence of Stochastic Parameter Variation," Econometrica, Econometric Society, vol. 44(1), pages 167-84, January. [Downloadable!] (restricted)
  11. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-56, July. [Downloadable!] (restricted)
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