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A Hidden Markov Model as a Dynamic Bayesian Classifier, With an Application to Forecasting Business-Cycle Turning Points

Author

Listed:
  • Koskinen, Lasse

    (National Institute of Economic Research)

  • Öller, Lars-Erik

    (National Institute of Economic Research)

Abstract

We introduce a method for dynamic classification of vector time series data into different regimes. A hidden Markov regime-switching model is used in classification. Past regimes are determined in advance and characterized by first and second moments of the observation vector. In estimation and model selection, instead of the maximum likelihood principle, we use Brier´s probability score making it possible to perform feature extraction, eg. noise-removing filtering. When calibrated to the forecast horizon, the method provides a simple and computationally efficient way to utilize leading information in forecasting regimes in time series. The method is applied on forecasting turning points of Sweden`s industrial production, where the Stock Market Index and a Business Tendency Survey series together express expectations, providing leading information. The method is also tested on forecasting the business cycle of the US, using GDP and the Department of Commerce Composite Index of Leading Indicators.

Suggested Citation

  • Koskinen, Lasse & Öller, Lars-Erik, 1998. "A Hidden Markov Model as a Dynamic Bayesian Classifier, With an Application to Forecasting Business-Cycle Turning Points," Working Papers 59, National Institute of Economic Research.
    • Handle: RePEc:hhs:nierwp:0059
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    Cited by:

    1. Lindström, Tomas, 2000. "Qualitative Survey Responses and Production over the Business Cycle," Working Paper Series 116, Sveriges Riksbank (Central Bank of Sweden).
    2. E. Andersson, 2002. "Monitoring cyclical processes. A non-parametric approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(7), pages 973-990.
    3. Ahmad Jafari-Samimi & Babak Shirazi & Hamed Fazlollahtabar, 2007. "A Comparison Between Time Series, Exponential Smoothing, and Neural Network Methods To Forecast GDPof Iran," Iranian Economic Review (IER), Faculty of Economics,University of Tehran.Tehran,Iran, vol. 12(2), pages 19-35, spring.

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