A Classifying Procedure for Signaling Turning Points

Lasse Koskinen and Lars-Erik Öller
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Lasse Koskinen: The Central Pension Security Institute, Postal: The Central Pension Security Institute, PL 00065, Helsinki, Finland,

No 427, SSE/EFI Working Paper Series in Economics and Finance from Stockholm School of Economics

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. (search for similar items in EconPapers)
JEL-codes: C22 C53 E37 (search for similar items in EconPapers)
Pages: 22 pages
Date: 2001-02-07
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Published in Journal of Forecasting, 2004, pages 197-214.

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Related works:
Journal Article: A classifying procedure for signalling turning points (2004)
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Persistent link: https://EconPapers.repec.org/RePEc:hhs:hastef:0427

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