This repository contains R codes to model and forecast age-at-death distributions using the C-STAD model, and reproduce the results shown in the paper published in Insurance: Mathematics and Economics. A pre-print version of the article is availble in this repository.
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 understandings of mortality developments by capturing peculiar cohort effects, which might be overlooked by a conventional age-period perspective. Moreover, mortality experience 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.
Basellini U., Kjærgaard S. and Camarda C.G. (2020). An age-at-death distribution approach to forecast cohort mortality. Insurance: Mathematics and Economics, 91, 129--143.