Modelling Frontier Mortality Using Bayesian Generalised Additive Models

Hilton Jason (), Dodd Erengul (), Forster Jonathan J. () and Smith Peter W.F. ()
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Hilton Jason: Department of Social Statistics and Demography, University of Southampton, Highfield, Southampton, SO17 1BJ, United Kingdom.
Dodd Erengul: School of Mathematical Sciences, University of Southampton, Highfield, Southampton, SO17 1BJ, United Kingdom.
Forster Jonathan J.: Department of Statistics, University of Warwick, Coventry, CV4 7AL, United Kingdom.
Smith Peter W.F.: Department of Social Statistics and Demography, University of Southampton, Highfield, Southampton, SO17 1BJ, United Kingdom.

Journal of Official Statistics, 2021, vol. 37, issue 3, 569-589

Abstract: Mortality rates differ across countries and years, and the country with the lowest observed mortality has changed over time. However, the classic Science paper by Oeppen and Vaupel (2002) identified a persistent linear trend over time in maximum national life expectancy. In this article, we look to exploit similar regularities in age-specific mortality by considering for any given year a hypothetical mortality ‘frontier’, which we define as the lower limit of the force of mortality at each age across all countries. Change in this frontier reflects incremental advances across the wide range of social, institutional and scientific dimensions that influence mortality. We jointly estimate frontier mortality as well as mortality rates for individual countries. Generalised additive models are used to estimate a smooth set of baseline frontier mortality rates and mortality improvements, and country-level mortality is modelled as a set of smooth, positive deviations from this, forcing the mortality estimates for individual countries to lie above the frontier. This model is fitted to data for a selection of countries from the Human Mortality Database (2019). The efficacy of the model in forecasting over a ten-year horizon is compared to a similar model fitted to each country separately.

Keywords: Mortality; demography; Bayesian methods; population forecasting (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:37:y:2021:i:3:p:569-589:n:8

DOI: 10.2478/jos-2021-0026

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