Specification and Informational Issues in Credit Scoring

29 Pages Posted: 12 Jan 2007

See all articles by Nicholas M. Kiefer

Nicholas M. Kiefer

Cornell University - Department of Economics

C. Erik Larson

Promontory Financial Group

Date Written: October 2006

Abstract

Lenders use rating and scoring models to rank credit applicants on their expected performance. The models and approaches are numerous. We explore the possibility that estimates generated by models developed with data drawn solely from extended loans are less valuable than they should be because of selectivity bias. We investigate the value of reject inference - methods that use a rejected applicant's characteristics, rather than loan performance data, in scoring model development. In the course of making this investigation, we also discuss the advantages of using parametric as well as nonparametric modeling. These issue are discussed and illustrated in the context of a simple stylized model.

Keywords: Logistic regression, specification testing, risk management, nonparametrics, reject inference

JEL Classification: C13, C14, C52, G11, G32

Suggested Citation

Kiefer, Nicholas M. and Larson, C. Erik, Specification and Informational Issues in Credit Scoring (October 2006). Available at SSRN: https://ssrn.com/abstract=956628 or http://dx.doi.org/10.2139/ssrn.956628

Nicholas M. Kiefer (Contact Author)

Cornell University - Department of Economics ( email )

490 Uris Hall
Ithaca, NY 14853-7601
United States

C. Erik Larson

Promontory Financial Group ( email )

1201 Pennsylvania Avenue, NW
Suite 617
Washington, DC 20004
United States
202-384-1200 (Phone)

HOME PAGE: http://www.ceriklarson.com

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