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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.

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  • 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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    1. S. J. Richards, 2008. "Detecting year‐of‐birth mortality patterns with limited data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(1), pages 279-298, January.
    2. 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.
    3. Carlos Wong-Fupuy & Steven Haberman, 2004. "Projecting Mortality Trends," North American Actuarial Journal, Taylor & Francis Journals, vol. 8(2), pages 56-83.
    4. Hatzopoulos, P. & Haberman, S., 2009. "A parameterized approach to modeling and forecasting mortality," Insurance: Mathematics and Economics, Elsevier, vol. 44(1), pages 103-123, February.
    5. Willets, R. C., 2004. "The Cohort Effect: Insights and Explanations," British Actuarial Journal, Cambridge University Press, vol. 10(4), pages 833-877, October.
    6. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737, May.
    7. Hatzopoulos, P. & Haberman, S., 2013. "Common mortality modeling and coherent forecasts. An empirical analysis of worldwide mortality data," Insurance: Mathematics and Economics, Elsevier, vol. 52(2), pages 320-337.
    8. Kirill F. Andreev & James W. Vaupel, 2006. "Forecasts of cohort mortality after age 50," MPIDR Working Papers WP-2006-012, Max Planck Institute for Demographic Research, Rostock, Germany.
    9. Renshaw, A.E. & Haberman, S., 2006. "A cohort-based extension to the Lee-Carter model for mortality reduction factors," Insurance: Mathematics and Economics, Elsevier, vol. 38(3), pages 556-570, June.
    10. Carter, Lawrence R. & Lee, Ronald D., 1992. "Modeling and forecasting US sex differentials in mortality," International Journal of Forecasting, Elsevier, vol. 8(3), pages 393-411, November.
    11. Kevin M. White, 2002. "Longevity Advances in High‐Income Countries, 1955–96," Population and Development Review, The Population Council, Inc., vol. 28(1), pages 59-76, March.
    12. Shiro Horiuchi & John Wilmoth, 1998. "Deceleration in the age pattern of mortality at olderages," Demography, Springer;Population Association of America (PAA), vol. 35(4), pages 391-412, November.
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    Cited by:

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    4. 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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