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On age-period-cohort parametric mortality rate projections

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  • Haberman, Steven
  • Renshaw, Arthur

Abstract

An enhanced version of the Lee-Carter modelling approach to mortality forecasting, which has been extended to include an age modulated cohort index in addition to the standard age modulated period index, is described and tested for prediction robustness. Life expectancy and annuity value predictions, at pensioner ages and for various periods are compared, both with and without the age modulated cohort index, for the England & Wales male mortality experience. The simulation of prediction intervals for these indices of interest is discussed in detail.

Suggested Citation

  • Haberman, Steven & Renshaw, Arthur, 2009. "On age-period-cohort parametric mortality rate projections," Insurance: Mathematics and Economics, Elsevier, vol. 45(2), pages 255-270, October.
    • Handle: RePEc:eee:insuma:v:45:y:2009:i:2:p:255-270
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    References listed on IDEAS

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    1. Andrew Cairns & David Blake & Kevin Dowd & Guy Coughlan & David Epstein & Alen Ong & Igor Balevich, 2009. "A Quantitative Comparison of Stochastic Mortality Models Using Data From England and Wales and the United States," North American Actuarial Journal, Taylor & Francis Journals, vol. 13(1), pages 1-35.
    2. Ronald Lee, 2000. "The Lee-Carter Method for Forecasting Mortality, with Various Extensions and Applications," North American Actuarial Journal, Taylor & Francis Journals, vol. 4(1), pages 80-91.
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    4. Renshaw, A.E. & Haberman, S., 2008. "On simulation-based approaches to risk measurement in mortality with specific reference to Poisson Lee-Carter modelling," Insurance: Mathematics and Economics, Elsevier, vol. 42(2), pages 797-816, April.
    5. 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.
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    Cited by:

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    7. Flici, Farid, 2015. "Mortality forecasting for the Algerian population with considering cohort effect," MPRA Paper 92173, University Library of Munich, Germany.
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    12. Beutner, Eric & Reese, Simon & Urbain, Jean-Pierre, 2017. "Identifiability issues of age–period and age–period–cohort models of the Lee–Carter type," Insurance: Mathematics and Economics, Elsevier, vol. 75(C), pages 117-125.
    13. Paola Biffi & Gian Clemente, 2014. "Selecting stochastic mortality models for the Italian population," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 37(2), pages 255-286, October.
    14. Lin, Tzuling & Tsai, Cary Chi-Liang, 2013. "On the mortality/longevity risk hedging with mortality immunization," Insurance: Mathematics and Economics, Elsevier, vol. 53(3), pages 580-596.
    15. Francisco Morillas & José Valero, 2021. "On a Retarded Nonlocal Ordinary Differential System with Discrete Diffusion Modeling Life Tables," Mathematics, MDPI, vol. 9(3), pages 1-27, January.
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    17. Hunt, Andrew & Villegas, Andrés M., 2015. "Robustness and convergence in the Lee–Carter model with cohort effects," Insurance: Mathematics and Economics, Elsevier, vol. 64(C), pages 186-202.
    18. Hiroki Masuda & Lorenzo Mercuri & Yuma Uehara, 2024. "Student t-L\'evy regression model in YUIMA," Papers 2403.12078, arXiv.org.
    19. Chou-Wen Wang & Hong-Chih Huang & I-Chien Liu, 2013. "Mortality Modeling With Non-Gaussian Innovations and Applications to the Valuation of Longevity Swaps," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 80(3), pages 775-798, September.
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    21. Ana Debón & Steven Haberman & Francisco Montes & Edoardo Otranto, 2021. "Do Different Models Induce Changes in Mortality Indicators? That Is a Key Question for Extending the Lee-Carter Model," IJERPH, MDPI, vol. 18(4), pages 1-16, February.
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    24. Carlo Giovanni Camarda, 2019. "Smooth constrained mortality forecasting," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(38), pages 1091-1130.

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