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http://hdl.handle.net/10419/56339
  
Title:A classifying procedure for signaling turning points PDF Logo
Authors:Koskinen, Lasse
Öller, Lars-Erik
Issue Date:2001
Series/Report no.:SSE/EFI Working Paper Series in Economics and Finance 427
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. Business Cycle ; Feature Extraction ; Hidden Markov Switching-Regime Model ; Leading Indicator ; Probability Forecast
JEL:C22
C53
E37
Document Type:Working Paper
Appears in Collections:SSE/EFI Working Paper Series in Economics and Finance, EFI - The Economic Research Institute, Stockholm School of Economics

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