Using Non-Parametric Count Model for Credit Scoring
| Autor: | Sami Mestiri, Abdeljelil Farhat |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Economics and Econometrics
050208 finance Variables media_common.quotation_subject 05 social sciences Economics Econometrics and Finance (miscellaneous) Negative binomial distribution Nonparametric statistics Development Poisson distribution Conditional expectation Regression symbols.namesake 0502 economics and business Statistics symbols Poisson regression 050207 economics Business and International Management Mathematics media_common Count data |
| Zdroj: | SSRN Electronic Journal. |
| ISSN: | 1556-5068 |
| DOI: | 10.2139/ssrn.3464812 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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