Using Non-Parametric Count Model for Credit Scoring

13 Pages Posted: 15 Oct 2019

See all articles by Sami Mestiri

Sami Mestiri

Research Unit: Applied Economics and Simulation

Abdeljelil Farhat

University of Monastir

Date Written: October 5, 2019

Abstract

The purpose of this paper is to apply count data models to predict the number of times a credit applicant will not pay the amount awarded to repay 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.

Keywords: count data; non-parametric model; credit scoring; prediction

JEL Classification: G17, G21, C14, C53

Suggested Citation

Mestiri, Sami and Farhat, Abdeljelil, Using Non-Parametric Count Model for Credit Scoring (October 5, 2019). Available at SSRN: https://ssrn.com/abstract=3464812 or http://dx.doi.org/10.2139/ssrn.3464812

Sami Mestiri (Contact Author)

Research Unit: Applied Economics and Simulation ( email )

Ibn Sina Hiboun
Mahdia-Tunisia, 5111
Tunisia

Abdeljelil Farhat

University of Monastir ( email )

Mounastir, 4100
Tunisia

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