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An age-at-death distribution approach to forecast cohort mortality

Author

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  • Ugofilippo Basellini
  • Søren Kjærgaard
  • Carlo Giovanni Camarda

Abstract

Mortality forecasting has received increasing interest during recent decades due to the negative financial effects of continuous longevity improvements on public and private institutions’ liabilities. However, little attention has been paid to forecasting mortality from a cohort perspective. In this article, we introduce a novel methodology to forecast adult cohort mortality from age-at-death distributions. We propose a relational model that associates a time-invariant standard to a series of fully and partially observed distributions. Relation is achieved via a transformation of the age-axis. We show that cohort forecasts can improve our understanding of mortality developments by capturing distinct cohort effects, which might be overlooked by a conventional age-period perspective. Moreover, mortality experiences of partially observed cohorts are routinely completed. We illustrate our methodology on adult female mortality for cohorts born between 1835 and 1970 in two high-longevity countries using data from the Human Mortality Database.

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  • Ugofilippo Basellini & Søren Kjærgaard & Carlo Giovanni Camarda, 2020. "An age-at-death distribution approach to forecast cohort mortality," Working Papers axafx5_3agsuwaphvlfk, French Institute for Demographic Studies.
    • Handle: RePEc:idg:wpaper:axafx5_3agsuwaphvlfk
    DOI: 10.1016/j.insmatheco.2020.01.007
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    References listed on IDEAS

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    Cited by:

    1. van Raalte, Alyson A & Basellini, Ugofilippo & Camarda, Carlo Giovanni & Nepomuceno, Marília & Myrskylä, Mikko, 2022. "The dangers of drawing cohort profiles from period data: a research note," SocArXiv frkcw, Center for Open Science.
    2. 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.
    3. Héctor Pifarré i Arolas & José C. Andrade Santacruz & Mikko Myrskylä, 2023. "An overlapping cohorts perspective of lifespan inequality," MPIDR Working Papers WP-2023-046, Max Planck Institute for Demographic Research, Rostock, Germany.
    4. Alyson van Raalte & Ugofilippo Basellini & Carlo Giovanni Camarda & Marília R. Nepomuceno & Mikko Myrskylä, 2022. "The dangers of drawing cohort profiles from period data: a research note," Working Papers ayadh-ohbnm4x3q6cor1, French Institute for Demographic Studies.
    5. Carlo G. Camarda & Ugofilippo Basellini, 2021. "Smoothing, Decomposing and Forecasting Mortality Rates," European Journal of Population, Springer;European Association for Population Studies, vol. 37(3), pages 569-602, July.
    6. Shang, Han Lin & Haberman, Steven & Xu, Ruofan, 2022. "Multi-population modelling and forecasting life-table death counts," Insurance: Mathematics and Economics, Elsevier, vol. 106(C), pages 239-253.

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    Keywords

    Mortality forecasting; Mortality modelling; Relational models; Cohort life table; Smoothing;
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