Journal of Forecasting · 2004 · 39 citations · 23 references
ComputingBusiness EconomicsEconomicsEconometricsForecastingTime Series AnalysisEconomic ForecastingHidden Markov ModelsTime Series Feature ExtractionTime Series ForecastingTime Series ModelingTime Series PredictionBayesian ForecastingMaximum Likelihood ClassificationMaximum Likelihood InferencePredictionRegime Change DetectionTurning Point AnalysisTurning Point ForecastingTurning Point Problems
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. Copyright © 2004 John Wiley & Sons, Ltd.
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Economic and statistical measures of forecast accuracy
Clive W. J. Granger, M. Hashem Pesaran · Journal of Forecasting · 2000 · 407 citations · Full text
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Forecasting non-stationary economic time series
Computers & Mathematics with Applications · 2000 · 352 citations