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Bayesian estimate of credit risk via MCMC with delayed rejection

Author

Listed:
  • Mira Antonietta

    (Department of Economics, University of Insubria, Italy)

  • Tenconi Paolo

    (University of Switzerland)

Abstract

We develop a Bayesian hierarchical logistic regression model to predict the credit risk of companiers classified in different sectors. Explanatory variables derived by experts from balance-sheets are included. Markov chain Monte Carlo (MCMC) methods are used to estimate the proposed model. In particular we show how the delaying rejection strategy outperforms the standart Metrtopolis-Hastings algorithm in terms of asymptotic efficiency of the resulting estimates. The advantages of our over others proposed in the literature are discussed and tested via cross-validation procedures.

Suggested Citation

  • Mira Antonietta & Tenconi Paolo, 2003. "Bayesian estimate of credit risk via MCMC with delayed rejection," Economics and Quantitative Methods qf0315, Department of Economics, University of Insubria.
    • Handle: RePEc:ins:quaeco:qf0315
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    File URL: https://www.eco.uninsubria.it/RePEc/pdf/QF2003_34.pdf
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    Cited by:

    1. V L Miguéis & D F Benoit & D Van den Poel, 2013. "Enhanced decision support in credit scoring using Bayesian binary quantile regression," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(9), pages 1374-1383, September.

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