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Predicting the Turning Points of Business and Economic Time Series

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  • Kling, John L

Abstract

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
    DOI: 10.1086/296393
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    Cited by:

    1. Barreiro Hurlé, Jesús & Pérez Y Pérez, Luis, 2006. "Social benefi ts of water quality improvement: an evaluation of the averting cost method in households/Benefi cios sociales de la mejora en la calidad del agua: una aproximación a partir de los costes," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 24, pages 453-476, Abril.
    2. Chua, Chew Lian & Tsiaplias, Sarantis, 2011. "Predicting economic contractions and expansions with the aid of professional forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 438-451.
    3. Chew Lian Chua & Sarantis Tsiaplias, 2009. "Can consumer sentiment and its components forecast Australian GDP and consumption?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(8), pages 698-711.
    4. Carriero, Andrea & Marcellino, Massimiliano, 2007. "A comparison of methods for the construction of composite coincident and leading indexes for the UK," International Journal of Forecasting, Elsevier, vol. 23(2), pages 219-236.
    5. Qi, Min, 2001. "Predicting US recessions with leading indicators via neural network models," International Journal of Forecasting, Elsevier, vol. 17(3), pages 383-401.
    6. Khurshid M. Kiani, 2007. "Asymmetric Business Cycle Fluctuations and Contagion Effects in G7 Countries," International Journal of Business and Economics, School of Management Development, 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. Khurshid Kiani, 2011. "Fluctuations in Economic and Activity and Stabilization Policies in the CIS," Computational Economics, Springer;Society for Computational Economics, vol. 37(2), pages 193-220, February.
    9. 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.
    10. 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.
    11. Carriero, Andrea & Marcellino, Massimiliano, 2007. "A comparison of methods for the construction of composite coincident and leading indexes for the UK," International Journal of Forecasting, Elsevier, vol. 23(2), pages 219-236.
    12. Chan Guk Huh, 1998. "Forecasting industrial production using models with business cycle asymmetry," Economic Review, Federal Reserve Bank of San Francisco, pages 29-41.
    13. 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.
    14. Christopher S. McIntosh & Jeffrey H. Dorfman, 1992. "Qualitative Forecast Evaluation: A Comparison of Two Performance Measures," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 74(1), pages 209-214.
    15. Andrew J. Filardo, 1999. "How reliable are recession prediction models?," Economic Review, Federal Reserve Bank of Kansas City, vol. 84(Q II), pages 35-55.
    16. Pablo Galaso & Sandra Rodriguez, 2014. "A composite leading cycle indicator for Uruguay," Documentos de Trabajo (working papers) 14-09, Instituto de Economía - IECON.
    17. Andrew Filardo, 2004. "The 2001 US recession: what did recession prediction models tell us?," BIS Working Papers 148, Bank for International Settlements.
    18. Victor Zarnowitz, 1986. "The Record and Improvability of Economic Forecasting," NBER Working Papers 2099, National Bureau of Economic Research, Inc.
    19. Barberá De La Torre, Rafael Antonio & Doncel Pedrera, Luis Miguel & Sainz González, Jorge, 2006. "On the predictibility of the exchange rate behaviour: An application of Lucas' Model to the Spanish case/¿Es posible predecir el comportamiento del tipo de cambio? Una aplicación del modelo de Lucas a," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 24, pages 427-452, Abril.

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