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Using Non-parametric Count Model for Credit Scoring

Author

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  • Sami Mestiri

    (University of Monastir)

  • Abdeljelil Farhat

    (University of Monastir)

Abstract

The purpose of this paper is to apply count data models to predict the number of times a borrower pays late the amount of the credit. Poisson models and negative binomial distribution models, taking into account the observed heterogeneity, are generally used in situations where the dependent variable is discrete. Alternatively, we propose to use non parametric model where the relationship form between conditional mean and the explanatory variables is unknown. The empirical results found suggest that the nonparametric poisson model regression has the best prediction of the number of default payment.

Suggested Citation

  • Sami Mestiri & Abdeljelil Farhat, 2021. "Using Non-parametric Count Model for Credit Scoring," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 39-49, March.
    • Handle: RePEc:spr:jqecon:v:19:y:2021:i:1:d:10.1007_s40953-020-00208-w
    DOI: 10.1007/s40953-020-00208-w
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    References listed on IDEAS

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    1. Hall, Bronwyn H & Griliches, Zvi & Hausman, Jerry A, 1986. "Patents and R and D: Is There a Lag?," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 27(2), pages 265-283, June.
    2. 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.
    3. Ichimura, Hidehiko & Todd, Petra E., 2007. "Implementing Nonparametric and Semiparametric Estimators," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 74, Elsevier.
    4. Hinde, John & Demetrio, Clarice G. B., 1998. "Overdispersion: Models and estimation," Computational Statistics & Data Analysis, Elsevier, vol. 27(2), pages 151-170, April.
    5. Mullahy, John, 1986. "Specification and testing of some modified count data models," Journal of Econometrics, Elsevier, vol. 33(3), pages 341-365, December.
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    Cited by:

    1. Yeh-Ching Low & Seng-Huat Ong, 2023. "Modelling of Loan Non-Payments with Count Distributions Arising from Non-Exponential Inter-Arrival Times," JRFM, MDPI, vol. 16(3), pages 1-14, February.
    2. Mestiri, Sami, 2024. "Financial applications of machine learning using R software," MPRA Paper 119998, University Library of Munich, Germany.
    3. Mestiri, Sami, 2023. "How to use machine learning in finance," MPRA Paper 120045, University Library of Munich, Germany.

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