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Modeling trends in cohort survival probabilities

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  • Hatzopoulos, P.
  • Haberman, S.

Abstract

A new dynamic parametric model is proposed for analyzing the cohort survival function. A one-factor parameterized polynomial in age effects, complementary log–log link and multinomial cohort responses are utilized, within the generalized linear models (GLM) framework. Sparse Principal component analysis (SPCA) is then applied to cohort dependent parameter estimates and provides (marginal) estimates for a two-factor structure. Modeling the two-factor residuals in a similar way, in age–time effects, provides estimates for the three-factor age–cohort–period model. An application is presented for Sweden, Norway, England & Wales and Denmark mortality experience.

Suggested Citation

  • Hatzopoulos, P. & Haberman, S., 2015. "Modeling trends in cohort survival probabilities," Insurance: Mathematics and Economics, Elsevier, vol. 64(C), pages 162-179.
    • Handle: RePEc:eee:insuma:v:64:y:2015:i:c:p:162-179
    DOI: 10.1016/j.insmatheco.2015.05.009
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    References listed on IDEAS

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    10. Hatzopoulos, P. & Haberman, S., 2011. "A dynamic parameterization modeling for the age-period-cohort mortality," Insurance: Mathematics and Economics, Elsevier, vol. 49(2), pages 155-174, September.
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    Cited by:

    1. Hans Oluf Hansen, 2015. "Modeling and projecting mortality. A new model of heterogeneity and selection in survivorship," Discussion Papers 15-16, University of Copenhagen. Department of Economics.
    2. Hua Chen & Yugang Ding & Ruixian Li & ShanShan Mou, 2023. "Family ties and commercial health insurance consumption in China," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 48(1), pages 247-265, January.
    3. Jacie Jia Liu, 2021. "A Study on Link Functions for Modelling and Forecasting Old-Age Survival Probabilities of Australia and New Zealand," Risks, MDPI, vol. 9(1), pages 1-18, January.

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