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Cohort effects in mortality modelling: a Bayesian state-space approach

Published online by Cambridge University Press:  11 June 2018

Man Chung Fung ,
Gareth W. Peters  and
Pavel V. Shevchenko Open the ORCID record for Pavel V. Shevchenko [Opens in a new window]
Man Chung Fung
Affiliation:
Data61, CSIRO, Sydney, NSW 2122, Australia
Gareth W. Peters
Affiliation:
Department of Actuarial Mathematics and Statistics, Heriot-Watt University, Edinburgh, EH14 4AS, UK
Pavel V. Shevchenko*
Affiliation:
Department of Applied Finance and Actuarial Studies, Macquarie University, Sydney, NSW 2109, Australia
*
*Correspondence to: Pavel V. Shevchenko, Department of Applied Finance and Actuarial Studies, Macquarie University, Sydney, NSW, Australia. E-mail: pavel.shevchenko@mq.edu.au
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Abstract

Cohort effects are important factors in determining the evolution of human mortality for certain countries. Extensions of dynamic mortality models with cohort features have been proposed in the literature to account for these factors under the generalised linear modelling framework. In this paper we approach the problem of mortality modelling with cohort factors incorporated through a novel formulation under a state-space methodology. In the process we demonstrate that cohort factors can be formulated naturally under the state-space framework, despite the fact that cohort factors are indexed according to year-of-birth rather than year. Bayesian inference for cohort models in a state-space formulation is then developed based on an efficient Markov chain Monte Carlo sampler, allowing for the quantification of parameter uncertainty in cohort models and resulting mortality forecasts that are used for life expectancy and life table constructions. The effectiveness of our approach is examined through comprehensive empirical studies involving male and female populations from various countries. Our results show that cohort patterns are present for certain countries that we studied and the inclusion of cohort factors are crucial in capturing these phenomena, thus highlighting the benefits of introducing cohort models in the state-space framework. Forecasting of cohort models is also discussed in light of the projection of cohort factors.

Keywords

Mortality modellingCohort featuresState-space modelBayesian inferenceMarkov chain Monte Carlo
Type
Paper
Information
Annals of Actuarial Science , Volume 13 , Issue 1 , March 2019 , pp. 109 - 144
Copyright
© Institute and Faculty of Actuaries 2018 

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