<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

Modelling Frontier Mortality Using Bayesian Generalised Additive Models

Department of Social Statistics and Demography

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"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...

Modelling Frontier Mortality Using Bayesian Generalised Additive Models

Data publikacji: 13 wrz 2021

Zakres stron: 569 - 589

Otrzymano: 01 lis 2019

Przyjęty: 01 lis 2020

DOI: https://doi.org/10.2478/jos-2021-0026

Słowa kluczowe
Mortality, demography, Bayesian methods, population forecasting

© 2021 Jason Hilton et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Modelling Frontier Mortality Using Bayesian Generalised Additive Models

Data publikacji: 13 wrz 2021

Zakres stron: 569 - 589

Otrzymano: 01 lis 2019

Przyjęty: 01 lis 2020

DOI: https://doi.org/10.2478/jos-2021-0026

Słowa kluczoweMortality, demography, Bayesian methods, population forecasting

© 2021 Jason Hilton et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
Mortality, demography, Bayesian methods, population forecasting