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

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  • Basellini, Ugofilippo
  • Kjærgaard, Søren
  • Camarda, Carlo Giovanni

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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  • Basellini, Ugofilippo & Kjærgaard, Søren & Camarda, Carlo Giovanni, 2020. "An age-at-death distribution approach to forecast cohort mortality," Insurance: Mathematics and Economics, Elsevier, vol. 91(C), pages 129-143.
    • Handle: RePEc:eee:insuma:v:91:y:2020:i:c:p:129-143
    DOI: 10.1016/j.insmatheco.2020.01.007
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    Cited by:

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    2. 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.
    3. Bravo, Jorge M. & Ayuso, Mercedes & Holzmann, Robert & Palmer, Edward, 2023. "Intergenerational actuarial fairness when longevity increases: Amending the retirement age," Insurance: Mathematics and Economics, Elsevier, vol. 113(C), pages 161-184.
    4. 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.
    5. 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.
    6. 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.
    7. 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.

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