Erschienen in:Rotman School of Management Working Paper ; No. 07-05
Umfang:
1 Online-Ressource (33 p)
Sprache:
Nicht zu entscheiden
DOI:
10.2139/ssrn.872541
Identifikator:
Entstehung:
Anmerkungen:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments November 25, 2006 erstellt
Beschreibung:
We model reject inference - inferring how a rejected credit applicant would have behaved had it been granted credit - using a maximum likelihood approach within the framework of missing data analysis. Contrary to other methods that impute missing data, this reject inference method embeds the missing data mechanism into model estimation directly. We test performance of three reject inference methods using real default data. Results show our method to be superior and to improve classification power for credit scoring in within-sample tests