Reject inference, augmentation, and sample selection

被引:46
作者
Banasik, John [1 ]
Crook, Jonathan [1 ]
机构
[1] Univ Edinburgh, Management Sch & Econ, Credit Res Ctr, Edinburgh EH8 9JY, Midlothian, Scotland
关键词
risk analysis; credit scoring; reject inference; augmentation; sample selection;
D O I
10.1016/j.ejor.2006.06.072
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
Many researchers see the need for reject inference in credit scoring models to come from a sample selection problem whereby a missing variable results in omitted variable bias. Alternatively, practitioners often see the problem as one of missing data where the relationship in the new model is biased because the behaviour of the omitted cases differs from that of those who make up the sample for a new model. To attempt to correct for this, differential weights are applied to the new cases. The aim of this paper is to see if the use of both a Heckman style sample selection model and the use of sampling weights, together, will improve predictive performance compared with either technique used alone. This paper will use a sample of applicants in which virtually every applicant was accepted. This allows us to compare the actual performance of each model with the performance of models which are based only on accepted cases. (c) 2006 Elsevier B.V. All rights reserved.
引用
收藏
页码:1582 / 1594
页数:13
相关论文
共 11 条
[1]  
[Anonymous], ANAL INCOMPLETE DATA
[2]   Credit scoring, augmentation and lean models [J].
Banasik, J ;
Crook, J .
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, 2005, 56 (09) :1072-1081
[3]   Sample selection bias in credit scoring models [J].
Banasik, J ;
Crook, J ;
Thomas, L .
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, 2003, 54 (08) :822-832
[4]   Does reject inference really improve the performance of application scoring models? [J].
Crook, J ;
Banasik, J .
JOURNAL OF BANKING & FINANCE, 2004, 28 (04) :857-874
[5]  
Feelders A. J., 2000, International Journal of Intelligent Systems in Accounting, Finance and Management, V9, P1, DOI 10.1002/(SICI)1099-1174(200003)9:1<1::AID-ISAF177>3.0.CO
[6]  
2-#
[7]  
HAND DJ, 1994, NEW APPROACHES CLASS, P292
[8]  
Heckman J, 1976, ANN EC SOCIAL MEASUR, V5
[9]  
Little R., 1987, STAT ANAL MISSING DA
[10]   ON THE COST OF PARTIAL OBSERVABILITY IN THE BIVARIATE PROBIT MODEL [J].
MENG, CL ;
SCHMIDT, P .
INTERNATIONAL ECONOMIC REVIEW, 1985, 26 (01) :71-85