CREDIT SCORING MODELING WITH STATE-DEPENDENT SAMPLE SELECTION: A COMPARISON STUDY WITH THE USUAL LOGISTIC MODELING
| Autor: | Carlos R. Diniz, P.H.D. Ferreira, Francisco Louzada |
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| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
INFERÊNCIA ESTATÍSTICA
credit scoring Discrete choice Logistic distribution lcsh:Mathematics Regression analysis bounded logistic regression Management Science and Operations Research performance measures Logistic regression lcsh:QA1-939 Logistic model tree logistic regression with state-dependent sample selection Statistics classification models Economics Econometrics naive logistic regression Regression diagnostic Factor regression model Multinomial logistic regression |
| Zdroj: | Pesquisa Operacional, Vol 35, Iss 1, Pp 39-56 (2015) Pesquisa Operacional, Volume: 35, Issue: 1, Pages: 39-56, Published: APR 2015 Pesquisa Operacional v.35 n.1 2015 Pesquisa operacional Sociedade Brasileira de Pesquisa Operacional (SOBRAPO) instacron:SOBRAPO Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual) Universidade de São Paulo (USP) instacron:USP |
| ISSN: | 1678-5142 |
| Popis: | Statistical methods have been widely employed to assess the capabilities of credit scoring classification models in order to reduce the risk of wrong decisions when granting credit facilities to clients. The predictive quality of a classification model can be evaluated based on measures such as sensitivity, specificity, predictive values, accuracy, correlation coefficients and information theoretical measures, such as relative entropy and mutual information. In this paper we analyze the performance of a naive logistic regression model, a logistic regression with state-dependent sample selection model and a bounded logistic regression model via a large simulation study. Also, as a case study, the methodology is illustrated on a data set extracted from a Brazilian retail bank portfolio. Our simulation results so far revealed that there is nostatistically significant difference in terms of predictive capacity among the naive logistic regression models, the logistic regression with state-dependent sample selection models and the bounded logistic regression models. However, there is difference between the distributions of the estimated default probabilities from these three statistical modeling techniques, with the naive logistic regression models and the boundedlogistic regression models always underestimating such probabilities, particularly in the presence of balanced samples. Which are common in practice. |
| Databáze: | OpenAIRE |
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