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A Classifying Procedure for Signaling Turning Points

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Author Info

  • Koskinen, Lasse

    (The Central Pension Security Institute)

  • Öller, Lars-Erik

    ()
    (National Institute of Economic Research)

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 o maximizing a 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 is tested by forecasting turning points in the Swedish and US economies, using leading data. Clear and early turning point signals are obtained, contrasting favourable with earlier HMM studies. Some theoretical arguments for this are given.

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Bibliographic Info

Paper provided by Stockholm School of Economics in its series Working Paper Series in Economics and Finance with number 427.

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Length: 22 pages
Date of creation: 07 Feb 2001
Date of revision:
Publication status: Published in Journal of Forecasting, 2004, pages 197-214.
Handle: RePEc:hhs:hastef:0427

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Related research

Keywords: Business Cycle; Feature Extraction; Hidden Markov Switching-Regime Model; Leading Indicator; Probability Forecast.;

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  1. 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.
  2. 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.
  3. 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..
  4. 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, January.
  5. 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.
  6. Artis, Michael J, 1993. "Turning Point Prediction for the UK using CSO Leading Indicators," CEPR Discussion Papers 833, C.E.P.R. Discussion Papers.
  7. Arturo Estrella & Frederic S. Mishkin, 1999. "Predicting U.S. Recessions: Financial Variables as Leading Indicators," NBER Working Papers 5379, National Bureau of Economic Research, Inc.
  8. 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.
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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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