A classifying procedure for signalling turning points

A classifying procedure for signalling turning points

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Article ID: iaor2008468
Country: United Kingdom
Volume: 23
Issue: 3
Start Page Number: 197
End Page Number: 214
Publication Date: Apr 2004
Journal: International Journal of Forecasting
Authors: ,
Keywords: financial, sets, time series & forecasting methods
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 of estimating past turning points using maximum 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, here called a ‘Markov Bayesian Classifier (MBC)’, is tested by forecasting turning points in the Swedish and US economies, using leading data. Clear and early turning point signals are obtained, contrasting favourably with earlier HMM studies. Some theoretical arguments for this are given.

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