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The Economic Research Institute, Stockholm School of Economics SSE/EFI Working Paper Series in Economics and Finance

No 427:
A Classifying Procedure for Signaling Turning Points

Lasse Koskinen and Lars-Erik Öller ()

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.

Keywords: Business Cycle; Feature Extraction; Hidden Markov Switching-Regime Model; Leading Indicator; Probability Forecast.; (follow links to similar papers)

JEL-Codes: C22; C53; E37; (follow links to similar papers)

22 pages, February 7, 2001

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This paper is published as:
Koskinen, Lasse and Lars-Erik Öller, (2004), 'A Classifying Procedure for Signaling Turning Points', Journal of Forecasting, pages 197-214



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