A hierarchical mixture cure model with unobserved heterogeneity for credit risk

被引：8

作者：

Dirick, Lore ^{[1
,2
]}

Claeskens, Gerda ^{[1
,2
]}

Vasnev, Andrey ^{[3
]}

Baesens, Bart ^{[4
]}

机构：

[1] Katholieke Univ Leuven, ORSTAT, Naamsestr 69, B-3000 Leuven, Belgium

[2] Katholieke Univ Leuven, Leuven Stat Res Ctr, Naamsestr 69, B-3000 Leuven, Belgium

[3] Univ Sydney, Business Sch, Abercrombie Bldg H70, Sydney, NSW 2006, Australia

[4] Katholieke Univ Leuven, Fac Econ & Business, LIRIS, Naamsestr 69, B-3000 Leuven, Belgium

来源：

ECONOMETRICS AND STATISTICS | 2022年 / 22卷

关键词：

Credit risk modeling; Competing risks; EM-algorithm; Mixture cure model; Survival analysis; Unobserved heterogeneity; AKAIKE INFORMATION CRITERION; COMPETING RISKS; MAXIMUM-LIKELIHOOD; STANDARD ERRORS; SURVIVAL; EM; REGRESSION; IDENTIFIABILITY; TERMINATION; SEM;

D O I：

10.1016/j.ecosta.2020.12.002

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

The specific nature of credit loan data requires the use of mixture cure models within the class of survival analysis tools. The constructed models allow for competing risks such as early repayment and default, and for incorporating maturity, expressed as an unsusceptible part of the population. A novel further extension of such models incorporates unobserved heterogeneity within the risk groups. A hierarchical expectation-maximization algorithm is derived to fit the models and standard errors are obtained. Simulations and a data analysis illustrate the applicability and benefits of these models, and in particular an improved event time estimation. (c) 2021 EcoSta Econometrics and Statistics. Published by Elsevier B.V. All rights reserved.

引用

页码：39 / 55

页数：17

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