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By Jones S., Hensher D.A. (eds.)

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The problem is that the sample is extremely unbalanced, with only 10 per cent of the observations defaulting. 5. 10 shows the effect with three alternative choices of the threshold value. 09487 is the sample proportion. 36 William H. 15 5225 214 5439 6464 329 6793 7675 494 8169 4278 782 5060 3039 667 3706 1828 502 2330 Total 9503 996 10499 Expected profit The final step in this part of the analysis is to construct the equation for expected profit from approving an application. 22). 25 per cent finance charge, plus one week’s float on repayment and an interest rate of 10 per cent.

Greene Conditioning variables xi might include income, credit history, the ratio of credit card burden to current income, and so on. If D is sufficiently large relative to the attributes, that is, if the individual is in trouble enough, they default. 12 The classification rule is Predict Di ¼ 1 if 8ð 0 xi Þ > P Ã ; ð1:6Þ where P Ã is a threshold value chosen by the analyst. 5 is usually used for P Ã under the reasoning that we should predict default if the model predicts that it is more likely than not.

The horizontal line is drawn at zero. The shading of the triangles shows the density of the points in the sample. 0 Model predictions of profits vs. default probabilities shows that the model predicts negative profits for most individuals whose estimated default probability exceeds roughly ten per cent. 5 for the threshold for predicting default is obviously far too high to be effective in this setting. ’s finding that applicants whose default probability exceeded nine per cent were generally associated with negative profits.

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