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Predicting the Turning Points of Business and Economic Time Series


  • Kling, John L


Standard linear least-squares prediction methods are not directly applicable to making probability statements about time series turning points. William F. Wecker suggested a method for extending the least-squares technique to allow computation of the probability distribution of turning points of a time series. Wecker's analysis was univariate and did not consider all sources of uncertainty (i.e., estimates of coefficients). The primary purpose of this paper is fourfold: (1) to extend Wecker's analysis to the case of the multiple time-series model; (2) to consider most sources of model uncertainty; (3) to test the procedure for reliability (method of calibrations); and (4) to demonstrate some interesting applications. Copyright 1987 by the University of Chicago.

Suggested Citation

  • Kling, John L, 1987. "Predicting the Turning Points of Business and Economic Time Series," The Journal of Business, University of Chicago Press, vol. 60(2), pages 201-238, April.
  • Handle: RePEc:ucp:jnlbus:v:60:y:1987:i:2:p:201-38

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    References listed on IDEAS

    1. Barro, Robert J & Sahasakul, Chaipat, 1983. "Measuring the Average Marginal Tax Rate from the Individual Income Tax," The Journal of Business, University of Chicago Press, vol. 56(4), pages 419-452, October.
    2. Roger H. Gordon, 1983. "Social Security And Labor Supply Incentives," Contemporary Economic Policy, Western Economic Association International, vol. 1(3), pages 16-22, April.
    3. Robert J. Barro & Chaipat Sahasakul, 1983. "Measuring the Average Marginal Tax Rates from Social Security and the Individual Income Tax," University of Chicago - George G. Stigler Center for Study of Economy and State 29, Chicago - Center for Study of Economy and State.
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    6. Khurshid M. Kiani, 2007. "Asymmetric Business Cycle Fluctuations and Contagion Effects in G7 Countries," International Journal of Business and Economics, College of Business and College of Finance, Feng Chia University, Taichung, Taiwan, vol. 6(3), pages 237-253, December.
    7. Kenneth W Clements & Grace Gao, 2013. "A Multi-Market Approach to Measuring the Cycle," Economics Discussion / Working Papers 13-16, The University of Western Australia, Department of Economics.
    8. 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.
    9. James H. Stock & Mark W. Watson, 1993. "A Procedure for Predicting Recessions with Leading Indicators: Econometric Issues and Recent Experience," NBER Chapters,in: Business Cycles, Indicators and Forecasting, pages 95-156 National Bureau of Economic Research, Inc.
    10. Chan Huh, 1998. "Forecasting industrial production using models with business cycle asymmetry," Economic Review, Federal Reserve Bank of San Francisco, pages 29-41.
    11. Franses, Philip Hans & Paap, Richard, 1999. "Does Seasonality Influence the Dating of Business Cycle Turning Points?," Journal of Macroeconomics, Elsevier, vol. 21(1), pages 79-92, January.
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    14. Pablo Galaso & Sandra Rodriguez, 2014. "A composite leading cycle indicator for Uruguay," Documentos de Trabajo (working papers) 14-09, Instituto de Economía - IECON.
    15. Andrew Filardo, 2004. "The 2001 US recession: what did recession prediction models tell us?," BIS Working Papers 148, Bank for International Settlements.
    16. Victor Zarnowitz, 1986. "The Record and Improvability of Economic Forecasting," NBER Working Papers 2099, National Bureau of Economic Research, Inc.
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