Classification-relevant Importance Measures for the West German Business Cycle

Ursula Garczarek, Claus Weihs and Daniel Enache

No 2005,37, Technical Reports from Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen

Abstract: 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.

Date: 2005
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.econstor.eu/bitstream/10419/22627/1/tr37-05.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zbw:sfb475:200537

Access Statistics for this paper

More papers in Technical Reports from Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().