European Business Schools Librarian's Group

SSE/EFI Working Paper Series in Economics and Finance,
Stockholm School of Economics

No 427: A Classifying Procedure for Signaling Turning Points

Lasse Koskinen and Lars-Erik Öller ()
Additional contact information
Lasse Koskinen: The Central Pension Security Institute, Postal: The Central Pension Security Institute, PL 00065, Helsinki, Finland,
Lars-Erik Öller: National Institute of Economic Research, Postal: National Institute of Economic Research, P.O. Box 3116, S-103 62 Stockholm, Sweden, and, 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.

JEL-codes: C22; C53; E37

22 pages, February 7, 2001

Full text files 

hastef0427.pdf PDF-file Full text

Download statistics

Questions (including download problems) about the papers in this series should be directed to Helena Lundin ()
Report other problems with accessing this service to Sune Karlsson ().

RePEc:hhs:hastef:0427This page generated on 2024-09-13 22:19:41.