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Dynamic Bayesian networks for classification of business cycles

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  • Sondhauss, Ursula
  • Weihs, Claus

Abstract

We use Dynamic Bayesian networks to classify business cycle phases. We compare classifiers generated by learning the Dynamic Bayesian network structure on different sets of admissible network structures. Included are sets of network structures of the Tree Augmented Naive Bayes (TAN) classifiers of Friedman, Geiger, and Goldszmidt (1997) adapted for dynamic domains. The performance of the developed classifiers on the given data was modest.

Suggested Citation

  • Sondhauss, Ursula & Weihs, Claus, 1999. "Dynamic Bayesian networks for classification of business cycles," Technical Reports 1999,17, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    • Handle: RePEc:zbw:sfb475:199917
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    References listed on IDEAS

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    1. Filardo, Andrew J, 1994. "Business-Cycle Phases and Their Transitional Dynamics," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 299-308, July.
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    1. Weihs, Claus & Sondhauss, Ursula, 2000. "Business phase classification and prediction: how to compare interpretability of classification methods?," Technical Reports 2000,24, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

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