By Jones S., Hensher D.A. (eds.)
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Additional info for Advances in credit risk modelling and corporate bankruptcy prediction
03 But, since MEDINC is already in the equation, as well as the individual income, one must conclude that these variables are picking up some other effect. The last variable in the equation is the selectivity correction described earlier. Its large t-statistic suggests that the sample selection correction is, indeed, warranted. The coefficient on LAMBDA estimates À ‰uw¾ u. 204. The negative value is surprising given the criteria that are probably used to determine cardholder status. , are already in the equation, it is unclear just what sign should have been expected.
Contrary to what intuition might suggest, we find that when spending levels are included as a component of the default probability, which seems quite plausible, the optimal loan size is relatively small. The model used for profit in this study is rudimentary. More detailed data on payment schedules would allow a more elaborate behavioural model of the consumer’s repayment decisions. Nonetheless, it seems reasonable to expect similar patterns to emerge in more detailed studies. Since, in spite of our earlier discussion, we continue to find that default probability is a crucial determinant of the results, it seems that the greatest payoff in terms of model development would be found here.
1 Variables used in analysis of credit card default Indicators CARDHLDR ¼ DEFAULT ¼ 1 for cardholders, 0 for denied applicants. 1 for defaulted on payment, 0 if not. Expenditure EXP1, EXP2, EXP3, . . , EXP12 ¼ monthly expenditure in most recent 12 months. Demographic and Socioeconomic, from Application AGE ¼ age in years and twelfths of a year. DEPNDNTs ¼ dependents, missing data converted to 1. OWNRENT ¼ indicators ¼ 1 if own home, 0 if rent. MNTHPRVAD ¼ months at previous address. PREVIOUS ¼ 1 if previous card holder.