Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/56339 
Erscheinungsjahr: 
2001
Schriftenreihe/Nr.: 
SSE/EFI Working Paper Series in Economics and Finance No. 427
Verlag: 
Stockholm School of Economics, The Economic Research Institute (EFI), Stockholm
Zusammenfassung: 
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. Business Cycle ; Feature Extraction ; Hidden Markov Switching-Regime Model ; Leading Indicator ; Probability Forecast
JEL: 
C22
C53
E37
Dokumentart: 
Working Paper

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