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Multivariate classification of business phases

Author

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  • Weihs, Claus
  • Röhl, Michael C.
  • Theis, Winfried

Abstract

We propose multivariate classification as a statistical tool to describe business cycles. These cycles are often analyzed as a univariate phenomenon in terms of GNP or industrial net production ignoring additional information in other economic variables. Multivariate classification overcomes these limitations by reducing dimension in a way suitable for human perception. Based on a four phase scheme (upswing, upper turning point, downswing, lower turning point) we demonstrate the potential of classification methods by determining the important economic variables (stylized facts) for the German business cycle.

Suggested Citation

  • Weihs, Claus & Röhl, Michael C. & Theis, Winfried, 1999. "Multivariate classification of business phases," Technical Reports 1999,26, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    • Handle: RePEc:zbw:sfb475:199926
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    References listed on IDEAS

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    1. Diebold, Francis X & Rudebusch, Glenn D, 1996. "Measuring Business Cycles: A Modern Perspective," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 67-77, February.
    2. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1, February.
    3. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    4. John R. Meyer & Daniel H. Weinberg, 1975. "On the Classification of Economic Fluctuations," NBER Chapters, in: Explorations in Economic Research, Volume 2, number 2, pages 167-202, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Pumplün, Constanze & Weihs, Claus & Preusser, Andrea, 2004. "Experimental Design for Variable Selection in data bases," Technical Reports 2004,72, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    2. Zucknick, Manuela & Weihs, Claus & Garczarek, Ursula, 2002. "Outliers and influence points in German business cycles," Technical Reports 2002,67, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

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