A hierarchical mixture cure model with unobserved heterogeneity for credit risk
| Autor: | Bart Baesens, Andrey Vasnev, Lore Dirick, Gerda Claeskens |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Statistics and Probability
Economics and Econometrics education.field_of_study Class (computer programming) Computer science 05 social sciences Population 01 natural sciences Maturity (finance) 010104 statistics & probability Standard error Risk groups Loan 0502 economics and business Econometrics 0101 mathematics Statistics Probability and Uncertainty education 050205 econometrics Credit risk Event (probability theory) |
| Popis: | 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 com-peting 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 hierar-chical 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. ispartof: Econometrics and Statistics vol:22 pages:39-55 status: published |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|