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

Ugofilippo Basellini, Søren Kjærgaard and Carlo Giovanni Camarda

Insurance: Mathematics and Economics, 2020, vol. 91, issue C, 129-143

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.

Keywords: Mortality forecasting; Mortality modelling; Relational models; Cohort life table; Smoothing (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:insuma:v:91:y:2020:i:c:p:129-143

DOI: 10.1016/j.insmatheco.2020.01.007

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