Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/22627 
Year of Publication: 
2005
Series/Report no.: 
Technical Report No. 2005,37
Publisher: 
Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen, Dortmund
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.
Document Type: 
Working Paper

Files in This Item:
File
Size
127.15 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.