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Credit Risk Prediction based on Bayesian estimation of logistic regression model with random effects

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  • Mestiri, Sami
  • Farhat, Abdejelil

Abstract

The aim of this current paper is to predict the credit risk of banks in Tunisia, over the period (2000-2005). For this purpose, two methods for the estimation of logistic regression model with random effects: Penalized Quasi Likelihood (PQL) method and Gibbs Sampler algorithm are applied. By using information on a sample of 528 Tunisian firms and 26 financial ratios,we show that Bayesian approach improves the quality of model predictions in terms of good classification as well as by the ROC curve result.

Suggested Citation

  • Mestiri, Sami & Farhat, Abdejelil, 2018. "Credit Risk Prediction based on Bayesian estimation of logistic regression model with random effects," MPRA Paper 119960, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:119960
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    References listed on IDEAS

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    1. 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, September.
    2. Manel Hamdi & Sami Mestiri, 2014. "Bankruptcy prediction for Tunisian firms : An application of semi-parametric logistic regression and neural networks approach," Economics Bulletin, AccessEcon, vol. 34(1), pages 133-143.
    3. Steenackers, A. & Goovaerts, M. J., 1989. "A credit scoring model for personal loans," Insurance: Mathematics and Economics, Elsevier, vol. 8(1), pages 31-34, March.
    4. 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.
    5. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    6. Posch Peter N. & Loeffler Gunter & Schoene Christiane, 2005. "Bayesian Methods for Improving Credit Scoring Models," Finance 0505024, University Library of Munich, Germany.
    7. Robert B. Avery & Raphael W. Bostic & Paul S. Calem & Glenn B. Canner, 2000. "Credit Scoring: Statistical Issues and Evidence from Credit-Bureau Files," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 28(3), pages 523-547.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Forecasting; Credit risk; Penalized Quasi Likelihood; Gibbs Sampler; Logistic regression with random effects; Curve ROC;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G2 - Financial Economics - - Financial Institutions and Services

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