By Jones S., Hensher D.A. (eds.)
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Extra resources for Advances in credit risk modelling and corporate bankruptcy prediction
This is a type of non-random sampling that has been widely documented in other contexts, and has been modelled in a counterpart to the study by Boyes et al. (1989). Choice-based sampling induces a bias in the estimation of discrete choice models. As has been shown by Manski and Lerman (1977) possible to mitigate the induced bias if one knows the true proportions that should apply in the sampling. 3. 24 William H. 1 Variables used in analysis of credit card default Indicators CARDHLDR ¼ DEFAULT ¼ 1 for cardholders, 0 for denied applicants.
Since cardholder status is generated by the credit scorer while the default indicator is generated later by the cardholder the observations are sequential, not simultaneous. As such, the model of Abowd and Farber (1982) might apply. But, the simpler censoring interpretation seems more appropriate. It turns out that the difference is only one of interpretation. ’s model (see their p. 26)) are the same. 21 A statistical model for credit scoring The statistical question is whether the sample selection into cardholder status is significantly related to the expenditure level of the individuals sampled.
Conventional wisdom in this setting is that the own/rent indicator for home ownership is the single most powerful predictor of whether an applicant will be given a credit card. We find no evidence of this in these data. Rather, as one might expect, what explains acceptance best is a higher income, fewer dependents, and a ‘clean’ credit file with numerous accounts at the reporting agency. Surprisingly, being employed longer at one’s current job appears not to increase the probability of approval, though being self-employed appears significantly to decrease it.
Advances in credit risk modelling and corporate bankruptcy prediction by Jones S., Hensher D.A. (eds.)