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A classifying procedure for signalling turning points

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  • Lars-Erik Öller

    (Statistics Sweden, Stockholm, Sweden, and Stockholm University, Sweden)

  • Lasse Koskinen

    (Insurance Supervisory Authority, Helsinki, Finland)

Abstract

A Hidden Markov Model (HMM) is used to classify an out-of-sample observation vector into either of two regimes. This leads to a procedure for making probability forecasts for changes of regimes in a time series, i.e. for turning points. Instead of estimating past turning points using maximum likelihood, the model is estimated with respect to known past regimes. This makes it possible to perform feature extraction and estimation for different forecasting horizons. The inference aspect is emphasized by including a penalty for a wrong decision in the cost function. The method, here called a 'Markov Bayesian Classifier (MBC)', is tested by forecasting turning points in the Swedish and US economies, using leading data. Clear and early turning point signals are obtained, contrasting favourably with earlier HMM studies. Some theoretical arguments for this are given. Copyright © 2004 John Wiley & Sons, Ltd.

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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 23 (2004)
Issue (Month): 3 ()
Pages: 197-214

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Handle: RePEc:jof:jforec:v:23:y:2004:i:3:p:197-214

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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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  1. Diebold, Francis X & Rudebusch, Glenn D, 1990. "A Nonparametric Investigation of Duration Dependence in the American Business Cycle," Journal of Political Economy, University of Chicago Press, vol. 98(3), pages 596-616, June.
  2. Arturo Estrella & Frederic S. Mishkin, 1998. "Predicting U.S. Recessions: Financial Variables As Leading Indicators," The Review of Economics and Statistics, MIT Press, vol. 80(1), pages 45-61, February.
  3. 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.
  4. Ivanova, Detelina & Lahiri, Kajal & Seitz, Franz, 2000. "Interest rate spreads as predictors of German inflation and business cycles," International Journal of Forecasting, Elsevier, vol. 16(1), pages 39-58.
  5. Gordon, Stephen, 1997. "Stochastic Trends, Deterministic Trends, and Business Cycle Turning Points," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(4), pages 411-34, July-Aug..
  6. Michael P. Clements & David F. Hendry, 2001. "Forecasting Non-Stationary Economic Time Series," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262531895, December.
  7. Hamilton, James D & Perez-Quiros, Gabriel, 1996. "What Do the Leading Indicators Lead?," The Journal of Business, University of Chicago Press, vol. 69(1), pages 27-49, January.
  8. Artis, Michael J, et al, 1995. "Turning Point Prediction for the UK Using CSO Leading Indicators," Oxford Economic Papers, Oxford University Press, vol. 47(3), pages 397-417, July.
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Cited by:
  1. Chow, Hwee Kwan & Choy, Keen Meng, 2006. "Forecasting the global electronics cycle with leading indicators: A Bayesian VAR approach," International Journal of Forecasting, Elsevier, vol. 22(2), pages 301-315.
  2. Hansson, Jesper & Jansson, Per & Lof, Marten, 2005. "Business survey data: Do they help in forecasting GDP growth?," International Journal of Forecasting, Elsevier, vol. 21(2), pages 377-389.

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