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Detecting year‐of‐birth mortality patterns with limited data

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  • S. J. Richards

Abstract

Summary. Late life mortality patterns are of crucial interest to actuaries assessing risk of longevity, most obviously for annuities and defined benefit pension schemes. The stability of public finances is also affected, as the governments have very substantial risk of longevity in the form of state benefits and public sector pension schemes. One important explanatory variable for late life mortality patterns is year of birth. Previous work has demonstrated various techniques for detecting such patterns, but always with long time series of mortality rates. The paper describes two alternative ways to detect such patterns, even with missing population data or the absence of a time series. The paper finds support for the idea that different birth cohorts have different rates of aging.

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  • 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.
    • Handle: RePEc:bla:jorssa:v:171:y:2008:i:1:p:279-298
    DOI: 10.1111/j.1467-985X.2007.00501.x
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    References listed on IDEAS

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    1. Hirotugu Akaike, 1987. "Factor analysis and AIC," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 317-332, September.
    2. 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:

    1. Andrew J. G. Cairns & David Blake & Kevin Dowd & Amy R. Kessler, 2016. "Phantoms never die: living with unreliable population data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(4), pages 975-1005, October.
    2. Michael Murphy, 2010. "Detecting year‐of‐birth mortality patterns with limited data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(4), pages 915-920, October.
    3. Blake, David & Cairns, Andrew J.G., 2021. "Longevity risk and capital markets: The 2019-20 update," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 395-439.
    4. Michael Murphy, 2010. "Reexamining the Dominance of Birth Cohort Effects on Mortality," Population and Development Review, The Population Council, Inc., vol. 36(2), pages 365-390, June.
    5. Liu, Yanxin & Li, Johnny Siu-Hang, 2018. "A strategy for hedging risks associated with period and cohort effects using q-forwards," Insurance: Mathematics and Economics, Elsevier, vol. 78(C), pages 267-285.
    6. Stephen Richards, 2010. "Author's response," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(4), pages 920-924, October.
    7. S⊘ren Kjærgaard & Yunus Emre Ergemen & Marie‐Pier Bergeron‐Boucher & Jim Oeppen & Malene Kallestrup‐Lamb, 2020. "Longevity forecasting by socio‐economic groups using compositional data analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1167-1187, June.
    8. Boumezoued, Alexandre & Elfassihi, Amal, 2021. "Mortality data correction in the absence of monthly fertility records," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 486-508.
    9. Hunt, Andrew & Blake, David, 2015. "Modelling longevity bonds: Analysing the Swiss Re Kortis bond," Insurance: Mathematics and Economics, Elsevier, vol. 63(C), pages 12-29.
    10. Alexandre Boumezoued & Amal Elfassihi, 2020. "Mortality data correction in the absence of monthly fertility records," Working Papers hal-02634631, HAL.
    11. Hatzopoulos, P. & Haberman, S., 2015. "Modeling trends in cohort survival probabilities," Insurance: Mathematics and Economics, Elsevier, vol. 64(C), pages 162-179.
    12. Fabrice Balland & Alexandre Boumezoued & Laurent Devineau & Marine Habart & Tom Popa, 2018. "Mortality data reliability in an internal model," Papers 1803.00464, arXiv.org.
    13. Fabrice Balland & Alexandre Boumezoued & Laurent Devineau & Marine Habart & Tom Popa, 2018. "Mortality data reliability in an internal model," Working Papers hal-01719216, HAL.

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