Kiefer, Nicholas M. (Cornell U and US Department of the Treasury) Larson, C. Erik (Fannie Mae)
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 issues are discussed and illustrated in the context of a simple stylized model.
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Paper provided by Cornell University, Center for Analytic Economics in its series Working Papers with number
06-11.
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Find related papers by JEL classification: C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Capital and Ownership Structure
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