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A Classifying Procedure for Signaling Turning Points

  • Koskinen, Lasse

    (The Central Pension Security Institute)

  • Öller, Lars-Erik

    ()

    (National Institute of Economic Research)

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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Paper provided by Stockholm School of Economics in its series SSE/EFI 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
Contact details of provider: Postal: The Economic Research Institute, Stockholm School of Economics, P.O. Box 6501, 113 83 Stockholm, Sweden
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  1. Francis X. Diebold & Glenn D. Rudebusch, 1988. "A nonparametric investigation of duration dependence in the American business cycle," Working Paper Series / Economic Activity Section 90, Board of Governors of the Federal Reserve System (U.S.).
  2. 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.
  3. Artis, Michael J, 1993. "Turning Point Prediction for the UK using CSO Leading Indicators," CEPR Discussion Papers 833, C.E.P.R. Discussion Papers.
  4. 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..
  5. 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.
  6. Arturo Estrella & Frederic S. Mishkin, 1996. "Predicting U.S. recessions: financial variables as leading indicators," Research Paper 9609, Federal Reserve Bank of New York.
  7. 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, June.
  8. 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.
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