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An Empirical Study of the Credit Market with Unobserved Consumer Typers

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  • Li Gan
  • Roberto Mosquera

Abstract

This paper proposes an econometric model to identify unobserved consumer types in the credit market. Consumers choose different amounts of loan because of differences in their time or risk preferences (types). Thus, the unconditional probability of default is modeled using a mixture density combining a type-conditioning default variable with a type-determining random variable. The model is estimated using individual-level consumer credit card information. The parameter estimates and statistical tests support this kind of specification. Furthermore, the model produces better out-of-sample predictions on the probability of default than traditional models; hence, it provides evidence of the existence of types in the consumer credit market.

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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 13873.

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Date of creation: Mar 2008
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Handle: RePEc:nbr:nberwo:13873

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  1. Hellmuth Milde & John G. Riley, 1986. "Signalling in Credit Markets," UCLA Economics Working Papers 334, UCLA Department of Economics.
  2. 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.
  3. Christopher R. Knittel & Victor Stango, 2003. "Price Ceilings as Focal Points for Tacit Collusion: Evidence from Credit Cards," American Economic Review, American Economic Association, vol. 93(5), pages 1703-1729, December.
  4. Edelstein, Robert H, 1975. "Improving the Selection of Credit Risks: An Analysis of a Commercial Bank Minority Lending Program," Journal of Finance, American Finance Association, vol. 30(1), pages 37-55, March.
  5. Spence, A Michael, 1973. "Job Market Signaling," The Quarterly Journal of Economics, MIT Press, vol. 87(3), pages 355-74, August.
  6. Feinstein, Jonathan S, 1990. "Detection Controlled Estimation," Journal of Law and Economics, University of Chicago Press, vol. 33(1), pages 233-76, April.
  7. Stahl, Dale II & Wilson, Paul W., 1994. "Experimental evidence on players' models of other players," Journal of Economic Behavior & Organization, Elsevier, vol. 25(3), pages 309-327, December.
  8. Jaffee, Dwight M & Russell, Thomas, 1976. "Imperfect Information, Uncertainty, and Credit Rationing," The Quarterly Journal of Economics, MIT Press, vol. 90(4), pages 651-66, November.
  9. Wiginton, John C., 1980. "A Note on the Comparison of Logit and Discriminant Models of Consumer Credit Behavior," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 15(03), pages 757-770, September.
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Cited by:
  1. Li Gan & Manuel A. Hernandez & Yanyan Liu, 2013. "Group Lending with Heterogeneous Types," NBER Working Papers 18847, National Bureau of Economic Research, Inc.
  2. Li Gan & Tarun Sabarwal & Shuoxun Zhang, 2010. "Personal Bankruptcy: Reconciling Adverse Events and Strategic Timing Hypotheses Using Heterogeneity in Filing Types," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201008, University of Kansas, Department of Economics, revised May 2011.
  3. Li Gan & Feng Huang & Adalbert Mayer, 2011. "A Simple Test of Private Information in the Insurance Markets with Heterogeneous Insurance Demand," NBER Working Papers 16738, National Bureau of Economic Research, Inc.

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