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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.

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Bibliographic Info

Article provided by University of Chicago Press in its journal Journal of Business.

Volume (Year): 60 (1987)
Issue (Month): 2 (April)
Pages: 201-38

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Handle: RePEc:ucp:jnlbus:v:60:y:1987:i:2:p:201-38

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Web page: http://www.journals.uchicago.edu/JB/

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Cited by:
  1. Victor Zarnowitz, 1987. "The Record and Improvability of Economic Forecasting," NBER Working Papers 2099, National Bureau of Economic Research, Inc.
  2. Andrea Carriero & Massimiliano Marcellino, 2007. "A Comparison of Methods for the Construction of Composite Coincident and Leading Indexes for the UK," Working Papers 590, Queen Mary, University of London, School of Economics and Finance.
  3. Andrew Filardo, 2004. "The 2001 US recession: what did recession prediction models tell us?," BIS Working Papers 148, Bank for International Settlements.
  4. 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 coste," Estudios de Economía Aplicada, Estudios de Economía Aplicada, vol. 24, pages 453-476, Abril.
  5. 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.
  6. Chan Huh, 1998. "Forecasting industrial production using models with business cycle asymmetry," Economic Review, Federal Reserve Bank of San Francisco, pages 29-41.
  7. 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.
  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. 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," Estudios de Economía Aplicada, Estudios de Economía Aplicada, vol. 24, pages 427-452, Abril.
  10. 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.
  11. Khurshid M. Kiani & Prasad V. Bidarkota & Terry L. Kastens, 2005. "Forecast performance of neural networks and business cycle asymmetries," Applied Financial Economics Letters, Taylor and Francis Journals, vol. 1(4), pages 205-210, July.
  12. Qi, Min, 2001. "Predicting US recessions with leading indicators via neural network models," International Journal of Forecasting, Elsevier, vol. 17(3), pages 383-401.
  13. Khurshid Kiani, 2011. "Fluctuations in Economic and Activity and Stabilization Policies in the CIS," Computational Economics, Society for Computational Economics, vol. 37(2), pages 193-220, February.

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