A hierarchical mixture cure model with unobserved heterogeneity for credit risk

Lore Dirick, Gerda Claeskens, Andrey Vasnev and Bart Baesens

Econometrics and Statistics, 2022, vol. 22, issue C, 39-55

Abstract: 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.

Keywords: Credit risk modeling; Competing risks; EM-algorithm; Mixture cure model; Survival analysis; Unobserved heterogeneity (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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Working Paper: A hierarchical mixture cure model with unobserved heterogeneity for credit risk (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:22:y:2022:i:c:p:39-55

DOI: 10.1016/j.ecosta.2020.12.002

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