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A Classifying Procedure for Signaling Turning Points

Listed author(s):
  • 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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File URL: http://swopec.hhs.se/hastef/papers/hastef0427.pdf
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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
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

Phone: +46-(0)8-736 90 00
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