Bücker, MichaelKrämer, Walter2011-01-122011-01-122011-01-12http://hdl.handle.net/2003/2755410.17877/DE290R-1109We 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.enDiscussion Paper / SFB 823 ; 01/2011Credit scoringLogistic regressionMissing dataReject inference310330620working paper