Bayesian stochastic mortality modeling for two populations

A. J G Cairns, David Blake, Kevin Dowd, Guy D. Coughlan, Marwa Khalaf-Allah

Research output: Contribution to journalArticle

119 Citations (Scopus)

Abstract

This paper introduces a new framework for modelling the joint development over time of mortality rates in a pair of related populations with the primary aim of producing consistent mortality forecasts for the two populations. The primary aim is achieved by combining a number of recent and novel developments in stochastic mortality modelling, but these, additionally, provide us with a number of side benefi ts and insights for stochastic mortality modelling. By way of example, we propose an Age-Period-Cohort model which incorporates a mean-reverting stochastic spread that allows for different trends in mortality improvement rates in the short-run, but parallel improvements in the long run. Second, we fi t the model using a Bayesian framework that allows us to combine estimation of the unobservable state variables and the parameters of the stochastic processes driving them into a single procedure. Key benefi ts of this include dampening down of the impact of Poisson variation in death counts, full allowance for parameter uncertainty, and the fl exibility to deal with missing data. The framework is designed for large populations coupled with a small sub-population and is applied to the England & Wales national and Continuous Mortality Investigation assured lives males populations. We compare and contrast results based on the twopopulation approach with single-population results. © 2011 by Astin Bulletin. All rights reserved.

Original languageEnglish
Pages (from-to)29-59
Number of pages31
JournalASTIN Bulletin: The Journal of the IAA
Volume41
Issue number1
DOIs
Publication statusPublished - 2011

Keywords

  • Age effect
  • Cohort effect
  • Markov chain Monte Carlo
  • Missing data
  • Parameter uncertainty
  • Period effect
  • Small sub-populations

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