Reject inference in consumer credit scoring with nonignorable missing data
No Thumbnail Available
Date
2011-01-12
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
We generalize an empirical likelihood approach to missing data to the case of consumer credit scoring and provide a Hausman test for nonignorability of the missings.
An application to recent consumer credit data shows that our model yields parameter
estimates which are significantly different (both statistically and economically)
from the case where customers who were refused credit are ignored.
Description
Table of contents
Keywords
Credit scoring, Logistic regression, Missing data, Reject inference