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Prediction Optimal Classification of Business Phases

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  • Weihs, Claus
  • Luebke, Karsten

Abstract

Linear Discriminant Analysis (LDA) performs well for classifica- tion of business phases – even though the premises of an LDA are not met. As the variables are highly correlated there are numerical as well as interpretational shortcomings. By transforming the classification problem to a regression setting both problems can be addressed by a computer-intensive prediction oriented method which also improves the classification performance.

Suggested Citation

  • Weihs, Claus & Luebke, Karsten, 2005. "Prediction Optimal Classification of Business Phases," Technical Reports 2005,41, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    • Handle: RePEc:zbw:sfb475:200541
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    File URL: https://www.econstor.eu/bitstream/10419/22631/1/tr41-05.pdf
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    References listed on IDEAS

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    1. Weihs, Claus & Garczarek, Ursula, 2002. "Stability of multivariate representation of business cycles over time," Technical Reports 2002,20, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    2. Weihs, Claus & Luebke, Karsten, 2004. "A Note on the Dimension of the Projection Space in a Latent Factor Regression Model with Application to Business Cycle Classification," Technical Reports 2004,29, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
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    Cited by:

    1. Czogiel, Irina & Luebke, Karsten & Zentgraf, Marc & Weihs, Claus, 2006. "Localized Linear Discriminant Analysis," Technical Reports 2006,10, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

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    1. Luebke, Karsten & Czogiel, Irina & Weihs, Claus, 2004. "Latent Factor Prediction Pursuit for Rank Deficient Regressors," Technical Reports 2004,75, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    2. Enache, Daniel & Weihs, Claus, 2004. "Importance Assessment of Correlated Predictors in Business Cycles Classification," Technical Reports 2004,66, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
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    4. Garczarek, Ursula & Weihs, Claus & Enache, Daniel, 2005. "Classification-relevant Importance Measures for the West German Business Cycle," Technical Reports 2005,37, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    5. Weihs, Claus & Luebke, Karsten, 2004. "A Note on the Dimension of the Projection Space in a Latent Factor Regression Model with Application to Business Cycle Classification," Technical Reports 2004,29, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

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