A classifying procedure for signaling turning points

Arbeitspapier

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

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: SSE/EFI Working Paper Series in Economics and Finance ; No. 427

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes ; Forecasting Models; Simulation Methods ; Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Koskinen, Lasse ; Öller, Lars-Erik
• Ereignis: Veröffentlichung
• (wer): Stockholm School of Economics, The Economic Research Institute (EFI)
• (wo): Stockholm
• (wann): 2001

Weitere Informationen

• Handle: http://hdl.handle.net/10419/56339
• Letzte Aktualisierung: 10.03.2025, 11:44 MEZ