This repository contains R codes to model and forecast age-at-death distributions using the 3C-STAD model, and reproduce the results shown in the chapter of the Springer book Developments in Demographic Forecasting .
Mortality forecasting has recently received growing interest as accurate projections of future lifespans are needed to ensure the solvency of insurance and pension providers. Several innovative stochastic methodologies have been proposed in most recent decades, the majority of them being based on age-specific mortality rates or on summary measures of the life table. The age-at-death distribution is an informative life-table function that provides readily available information on the mortality pattern of a population, yet it has been mostly overlooked for mortality projections. In this chapter, we propose to analyse and forecast mortality developments over age and time by introducing a novel methodology based on age-at-death distributions. Our approach starts from a nonparametric decomposition of the mortality pattern into three independent components corresponding to childhood, early adulthood and senescence, respectively. We then model the evolution of each component-specific death density with a relational model that associates a time-invariant standard to a series of observed distributions by means of a transformation of the age axis. Our approach allows us to capture mortality developments over age and time, and forecasts can be derived from parameters' extrapolation using standard time series models. We illustrate our methods by estimating and forecasting the mortality pattern of females and males in two low-mortality countries using data of the Human Mortality Database. We compare the forecast accuracy of our model and its forecasts until 2050 with three other forecasting methodologies.
Basellini U., and Camarda C.G. (2020). A Three-component Approach to Model and Forecast Age-at-death Distributions. In Mazzuco, S. , and Keilman, N. (eds.), Developments in Demographic Forecasting, Springer, Cham. Available at this web link (open access).