Reject inference in consumer credit scoring with nonignorable missing data

dc.contributor.authorBücker, Michael
dc.contributor.authorKrämer, Walter
dc.date.accessioned2011-01-12T13:02:01Z
dc.date.available2011-01-12T13:02:01Z
dc.date.issued2011-01-12
dc.description.abstractWe 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.en
dc.identifier.urihttp://hdl.handle.net/2003/27554
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-1109
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB 823 ; 01/2011
dc.relation.isreplacedbyhttp://hdl.handle.net/2003/27636
dc.subjectCredit scoringen
dc.subjectLogistic regressionen
dc.subjectMissing dataen
dc.subjectReject inferenceen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titleReject inference in consumer credit scoring with nonignorable missing dataen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access

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