Modelling frontier mortality using Bayesian generalised additive models
Modelling frontier mortality using Bayesian generalised additive models
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.
Bayesian methods, Mortality, demography, population forecasting
569-589
Hilton, Jason
da31e515-1e34-4e9f-846d-633176bb3931
Dodd, Erengul
b3faed76-f22b-4928-a922-7f0b8439030d
Forster, Jonathan
e3c534ad-fa69-42f5-b67b-11617bc84879
Smith, Peter W.F.
961a01a3-bf4c-43ca-9599-5be4fd5d3940
13 September 2021
Hilton, Jason
da31e515-1e34-4e9f-846d-633176bb3931
Dodd, Erengul
b3faed76-f22b-4928-a922-7f0b8439030d
Forster, Jonathan
e3c534ad-fa69-42f5-b67b-11617bc84879
Smith, Peter W.F.
961a01a3-bf4c-43ca-9599-5be4fd5d3940
Hilton, Jason, Dodd, Erengul, Forster, Jonathan and Smith, Peter W.F.
(2021)
Modelling frontier mortality using Bayesian generalised additive models.
Journal of Official Statistics, 37 (3), .
(doi:10.2478/jos-2021-0026).
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.
Text
HiltonEtAl_Frontier_mortality
- Accepted Manuscript
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Accepted/In Press date: 21 October 2020
Published date: 13 September 2021
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© 2021 Jason Hilton et al., published by Sciendo.
Keywords:
Bayesian methods, Mortality, demography, population forecasting
Identifiers
Local EPrints ID: 444930
URI: http://eprints.soton.ac.uk/id/eprint/444930
ISSN: 0282-423X
PURE UUID: 5b403751-846a-43c7-a801-6cfaa1ef0e0e
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Date deposited: 12 Nov 2020 17:31
Last modified: 17 Mar 2024 06:02
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Author:
Jonathan Forster
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