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Multiple Event Incidence And Duration Analysis For Credit Data Incorporating Non‐Stochastic Loan Maturity

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  • John G. T. Watkins
  • Andrey L. Vasnev
  • Richard Gerlach

Abstract

SUMMARY Applications of duration analysis in economics and finance exclusively employ methods for events of stochastic duration. In application to credit data, previous research incorrectly treats the time to predetermined maturity events as censored stochastic event times. The medical literature has binary parametric ‘cure rate’ models that deal with populations that never experienced the modelled event. We propose and develop a multinomial parametric incidence and duration model, incorporating such populations. In the class of cure rate models, this is the first fully parametric multinomial model and is the first framework to accommodate an event with predetermined duration. The methodology is applied to unsecured personal loan credit data provided by one of Australia's largest financial services organizations. This framework is shown to be more flexible and predictive through a simulation and empirical study that reveals: simulation results of estimated parameters with a large reduction in bias; superior forecasting of duration; explanatory variables can act in different directions upon incidence and duration; and variables exist that are statistically significant in explaining only incidence or duration. Copyright © 2013 John Wiley & Sons, Ltd.

Suggested Citation

  • John G. T. Watkins & Andrey L. Vasnev & Richard Gerlach, 2014. "Multiple Event Incidence And Duration Analysis For Credit Data Incorporating Non‐Stochastic Loan Maturity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(4), pages 627-648, June.
    • Handle: RePEc:wly:japmet:v:29:y:2014:i:4:p:627-648
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    Cited by:

    1. Ewa Wycinka & Tomasz Jurkiewicz, 2019. "Survival Regression Models For Single Events And Competing Risks Based On Pseudoobservations," Statistics in Transition New Series, Polish Statistical Association, vol. 20(1), pages 171-188, March.
    2. Matthew Read & Chris Stewart & Gianni La Cava, 2014. "Mortgage-related Financial Difficulties: Evidence from Australian Micro-level Data," RBA Research Discussion Papers rdp2014-13, Reserve Bank of Australia.
    3. Lore Dirick & Gerda Claeskens & Bart Baesens, 2017. "Time to default in credit scoring using survival analysis: a benchmark study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(6), pages 652-665, June.
    4. Ewa Wycinka, 2015. "Modelling Time to Default Or Early Repayment as Competing Risks (Modelowanie czasu do zaprzestania splat rat kredytu lub wczesniejszej splaty kredytu jako zdarzen konkurujacych )," Problemy Zarzadzania, University of Warsaw, Faculty of Management, vol. 13(55), pages 146-157.
    5. Dirick, Lore & Claeskens, Gerda & Vasnev, Andrey & Baesens, Bart, 2022. "A hierarchical mixture cure model with unobserved heterogeneity for credit risk," Econometrics and Statistics, Elsevier, vol. 22(C), pages 39-55.
    6. Wycinka Ewa & Jurkiewicz Tomasz, 2019. "Survival Regression Models For Single Events And Competing Risks Based On Pseudo-Observations," Statistics in Transition New Series, Statistics Poland, vol. 20(1), pages 171-188, March.
    7. Dirick, Lore & Claeskens, Gerda & Baesens, Bart, 2015. "An Akaike information criterion for multiple event mixture cure models," European Journal of Operational Research, Elsevier, vol. 241(2), pages 449-457.

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