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Fitting multi-population mortality models to socio-economic groups

Published online by Cambridge University Press:  14 July 2020

Jie Wen ,
Andrew J.G. Cairns  and
Torsten Kleinow Open the ORCID record for Torsten Kleinow [Opens in a new window]
Jie Wen
Affiliation:
Department of Actuarial Mathematics and Statistics and the Maxwell Institute for Mathematical Sciences, School of Mathematical and Computer Sciences, Heriot-Watt University, EH14 4AS, Edinburgh, UK
Andrew J.G. Cairns
Affiliation:
Department of Actuarial Mathematics and Statistics and the Maxwell Institute for Mathematical Sciences, School of Mathematical and Computer Sciences, Heriot-Watt University, EH14 4AS, Edinburgh, UK
Torsten Kleinow*
Affiliation:
Department of Actuarial Mathematics and Statistics and the Maxwell Institute for Mathematical Sciences, School of Mathematical and Computer Sciences, Heriot-Watt University, EH14 4AS, Edinburgh, UK
*
*Corresponding author. E-mail: T.Kleinow@hw.ac.uk
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Abstract

We compare results for 12 multi-population mortality models fitted to 10 distinct socio-economic groups in England, subdivided using the Index of Multiple Deprivation. Using the Bayes Information Criterion to compare models, we find that a special case of the common age effect (CAE) model fits best in a variety of situations, achieving the best balance between goodness of fit and parsimony. We provide a detailed discussion of key models to highlight which features are important. Group-specific period effects are found to be more important than group-specific age effects, and non-parametric age effects deliver significantly better results than parametric (e.g. linear) age effects. We also find that the addition of cohort effects is beneficial in some cases but not all. The preferred CAE model has the additional benefit of being coherent in the sense of Hyndman et al. ((2013) Demography50(1), 261–283); some of the other models considered are not.

Keywords

Multi-population mortality modelsDeprivationMortality inequalitySocio-economic models
Type
Paper
Information
Annals of Actuarial Science , Volume 15 , Issue 1 , March 2021 , pp. 144 - 172
Copyright
© Institute and Faculty of Actuaries 2020

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