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Estimation of default probabilities using incomplete contracts data

  • Santos Silva, J.M.C.
  • Murteira, J.M.R.

This paper develops a count data model for credit scoring which allows the estimation of default probabilities using incomplete contracts data. The main advantage of the proposed approach is that it permits a more efficient use of the data, including that for the most recent clients. Moreover, because the probability of default is specified as a function of the age of the contract, the model provides some information on the timing of the defaults. The model is based on the beta-binomial distribution, which is found to be particularly adequate for this purpose. A well-known dataset on personal loans is used to illustrate the application of the proposed model.

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Article provided by Elsevier in its journal Journal of Empirical Finance.

Volume (Year): 16 (2009)
Issue (Month): 3 (June)
Pages: 457-465

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Handle: RePEc:eee:empfin:v:16:y:2009:i:3:p:457-465
Contact details of provider: Web page: http://www.elsevier.com/locate/jempfin

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  1. Jeffrey M. Wooldridge, 1999. "Asymptotic Properties of Weighted M-Estimators for Variable Probability Samples," Econometrica, Econometric Society, vol. 67(6), pages 1385-1406, November.
  2. Wooldridge, Jeffrey M, 1992. "Some Alternatives to the Box-Cox Regression Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 33(4), pages 935-55, November.
  3. Roszbach, Kasper, 1998. "Bank Lending Policy, Credit Scoring and the Survival of Loans," SSE/EFI Working Paper Series in Economics and Finance 261, Stockholm School of Economics.
  4. James J. Heckman & Robert J. Willis, 1975. "A Beta-Logistic Model for the Analysis of Sequential Labor Force Participation by Married Women," NBER Working Papers 0112, National Bureau of Economic Research, Inc.
  5. Johansson, Per & Palme, Marten, 1996. "Do economic incentives affect work absence? Empirical evidence using Swedish micro data," Journal of Public Economics, Elsevier, vol. 59(2), pages 195-218, February.
  6. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-24, January.
  7. Mullahy, John, 1986. "Specification and testing of some modified count data models," Journal of Econometrics, Elsevier, vol. 33(3), pages 341-365, December.
  8. Dionne, Georges & Artis, Manuel & Guillen, Montserrat, 1996. "Count data models for a credit scoring system," Journal of Empirical Finance, Elsevier, vol. 3(3), pages 303-325, September.
  9. 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.
  10. Carling, Kenneth & Jacobson, Tor & Roszbach, Kasper, 1998. "Duration of consumer loans and bank lending policy: dormancy versus default risk," SSE/EFI Working Paper Series in Economics and Finance 280, Stockholm School of Economics.
  11. Carling, Kenneth & Jacobson, Tor & Roszbach, Kasper, 2001. "Dormancy risk and expected profits of consumer loans," Journal of Banking & Finance, Elsevier, vol. 25(4), pages 717-739, April.
  12. Thomas, Lyn C., 2000. "A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers," International Journal of Forecasting, Elsevier, vol. 16(2), pages 149-172.
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