Cohort effects in mortality modelling

a Bayesian state-space approach

Man Chung Fung, Gareth W. Peters, Pavel V. Shevchenko

Research output: Contribution to journalArticle

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.
Original languageEnglish
Pages (from-to)109-144
Number of pages36
JournalAnnals of Actuarial Science
Volume13
Issue number1
Early online date11 Jun 2018
DOIs
Publication statusPublished - Mar 2019

Keywords

  • Mortality modelling
  • Cohort features
  • State-space model
  • Bayesian inference
  • Markov chain Monte Carlo

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