By Jones S., Hensher D.A. (eds.)
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Our data contain no information about finance charges incurred or paid. We have only the expenditure levels and the default indicator. 23 A statistical model for credit scoring model produces a prescription for considerably higher acceptance rates for loan applicants than are seen in our observed data. 4. Data used in the application The models described earlier were estimated for a well known credit card company. The data set used in estimation consisted of 13,444 observations on credit card applications received in a single month in 1988.
11 lists the sample averages for E for several subgroups. 11 are striking. It is clear that the results are being driven by the default probability. 1 shows the behaviour of the model’s predictions of estimated profits against the predicted default probability for the full sample of individual observations. The dashed vertical line in the figure is drawn at the sample average default rate of slightly under 10 per cent. The horizontal line is drawn at zero. The shading of the triangles shows the density of the points in the sample.
These are counts, so the marginal effects are obtained directly. Note, in particular, the number of trade lines past due at the time of the application. 11). CPTF30, the number of 30 day delinquencies, is similarly influential. The marginal effects in the conditional probability, account for the selection equation. Let the joint probability be denoted Prob½D ¼ 1; C ¼ 1 ¼ 82 ½d; c; ; ð1:27Þ where d ¼ 0 xi þ ½0 z þ ! 21). 14)). Then, reconfigure ß, , and ﬁ conformably, with zeros in the locations where variables do not actually appear in the original equation.
Advances in credit risk modelling and corporate bankruptcy prediction by Jones S., Hensher D.A. (eds.)
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