Author:

Keywords:

Social Sciences, Science & Technology, Physical Sciences, Economics, Statistics & Probability, Business & Economics, Mathematics, 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, 3802 Econometrics, 4905 Statistics

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 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.