A Three-Component Approach to Model and Forecast Age-at-Death Distributions

Ugofilippo Basellini () and Carlo Giovanni Camarda
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Ugofilippo Basellini: Max Planck Institute for Demographic Research (MPIDR)
Carlo Giovanni Camarda: Institut national d’études démographiques (INED)

Chapter Chapter 6 in Developments in Demographic Forecasting, 2020, pp 105-129 from Springer

Abstract: Abstract 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 high-longevity countries using data of the Human Mortality Database. We compare the forecast accuracy of our model and its projections until 2050 with three other forecasting methodologies.

Keywords: Mortality forecasting; Mortality modelling; Relational models; Smoothing; Mortality decomposition; Life expectancy; Lifespan variability (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:ssdmcp:978-3-030-42472-5_6

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DOI: 10.1007/978-3-030-42472-5_6

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