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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

    as
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    6. Posch Peter N. & Loeffler Gunter & Schoene Christiane, 2005. "Bayesian Methods for Improving Credit Scoring Models," Finance 0505024, University Library of Munich, Germany.
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    Keywords

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    JEL classification:

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

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