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The construction of empirical credit scoring rules based on maximization principles

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  • Lieli, Robert P.
  • White, Halbert

Abstract

We examine the econometric implications of the decision problem faced by a profit/utility-maximizing lender operating in a simple "double-binary" environment, where the two actions available are "approve" or "reject", and the two states of the world are "pay back" or "default". In practice, such decisions are often made by applying a fixed cutoff to the maximum likelihood estimate of a parametric model of the default probability. Following (Elliott and Lieli, 2007), we argue that this practice might contradict the lender's economic objective and, using German loan data, we illustrate the use of "context-specific" cutoffs and an estimation method derived directly from the lender's problem. We also provide a brief discussion of how to incorporate legal constraints, such as the prohibition of disparate treatment of potential borrowers, into the lender's problem.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 157 (2010)
Issue (Month): 1 (July)
Pages: 110-119

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Handle: RePEc:eee:econom:v:157:y:2010:i:1:p:110-119

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Web page: http://www.elsevier.com/locate/jeconom

Related research

Keywords: Credit scoring Binary variables Profit maximization;

References

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  1. Becker, Gary S., 1971. "The Economics of Discrimination," University of Chicago Press Economics Books, University of Chicago Press, edition 1, number 9780226041162, March.
  2. Davidson, Russell & MacKinnon, James G., 1984. "Convenient specification tests for logit and probit models," Journal of Econometrics, Elsevier, vol. 25(3), pages 241-262, July.
  3. Manski, Charles F., 1985. "Semiparametric analysis of discrete response : Asymptotic properties of the maximum score estimator," Journal of Econometrics, Elsevier, vol. 27(3), pages 313-333, March.
  4. Helen F. Ladd, 1998. "Evidence on Discrimination in Mortgage Lending," Journal of Economic Perspectives, American Economic Association, vol. 12(2), pages 41-62, Spring.
  5. D. J. Hand & W. E. Henley, 1997. "Statistical Classification Methods in Consumer Credit Scoring: a Review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 160(3), pages 523-541.
  6. Goffe, William L. & Ferrier, Gary D. & Rogers, John, 1994. "Global optimization of statistical functions with simulated annealing," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 65-99.
  7. Manski, Charles F., 1975. "Maximum score estimation of the stochastic utility model of choice," Journal of Econometrics, Elsevier, vol. 3(3), pages 205-228, August.
  8. Phelps, Edmund S, 1972. "The Statistical Theory of Racism and Sexism," American Economic Review, American Economic Association, vol. 62(4), pages 659-61, September.
  9. Crook, Jonathan & Banasik, John, 2004. "Does reject inference really improve the performance of application scoring models?," Journal of Banking & Finance, Elsevier, vol. 28(4), pages 857-874, April.
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Cited by:
  1. Halbert White & Karim Chalak, 2008. "Identifying Structural Effects in Nonseparable Systems Using Covariates," Boston College Working Papers in Economics 734, Boston College Department of Economics.

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