Arbeitspapier
Classification-relevant Importance Measures for the West German Business Cycle
When analyzing business cycle data, one observes that the relevant predictor variables are often highly correlated. This paper presents a method to obtain measures of importance for the classification of data in which such multicollinearity is present. In systems with highly correlated variables it is interesting to know what changes are inflicted when a certain predictor is changed by one unit and all other predictors according to their correlation to the first instead of a ceteris paribus analysis. The approach described in this paper uses directional derivatives to obtain such importance measures. It is shown how the interesting directions can be estimated and different evaluation strategies for characteristics of classification models are presented. The method is then applied to linear discriminant analysis and multinomial logit for the classification of west German business cycle phases.
- Language
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Englisch
- Bibliographic citation
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Series: Technical Report ; No. 2005,37
- Subject
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Konjunktur
Clusteranalyse
Konjunkturindikator
Theorie
Zeitreihenanalyse
Deutschland
- Event
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Geistige Schöpfung
- (who)
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Garczarek, Ursula
Weihs, Claus
Enache, Daniel
- Event
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Veröffentlichung
- (who)
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Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
- (where)
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Dortmund
- (when)
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2005
- Handle
- Last update
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10.03.2025, 11:44 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Garczarek, Ursula
- Weihs, Claus
- Enache, Daniel
- Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
Time of origin
- 2005
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Importance Assessment of Correlated Predictors in Business Cycles Classification
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Classification and Clustering in Business Cycle Analysis
Dynamic Bayesian networks for classification of business cycles
Direct minimization of error rates in multivariate classification
Local Modelling in Classification on Different Feature Subspaces
Combining mental fit and data fit for classification rule selection
Determination of Relevant Frequencies and Modeling Varying Amplitudes of Harmonic Processes
Register Classification by Timbre
A Note on the Dimension of the Projection Space in a Latent Factor Regression Model with Application to Business Cycle Classification
Multivariate classification of business phases
Importance Assessment of Correlated Predictors in Business Cycles Classification
Business phase classification and prediction: how to compare interpretability of classification methods?
Prediction Optimal Classification of Business Phases
Classification and Clustering in Business Cycle Analysis
Dynamic Bayesian networks for classification of business cycles
Direct minimization of error rates in multivariate classification
Local Modelling in Classification on Different Feature Subspaces
Combining mental fit and data fit for classification rule selection
Determination of Relevant Frequencies and Modeling Varying Amplitudes of Harmonic Processes
Register Classification by Timbre
A Note on the Dimension of the Projection Space in a Latent Factor Regression Model with Application to Business Cycle Classification