Cohort Effects in Mortality Modelling: A Bayesian State-Space Approach

45 Pages Posted: 17 Apr 2018

See all articles by Simon Man Chung Fung

Simon Man Chung Fung

Commonwealth Bank of Australia

Gareth Peters

Department of Actuarial Mathematics and Statistics, Heriot-Watt University; University College London - Department of Statistical Science; University of Oxford - Oxford-Man Institute of Quantitative Finance; London School of Economics & Political Science (LSE) - Systemic Risk Centre; University of New South Wales (UNSW) - Faculty of Science

Pavel V. Shevchenko

Macquarie University; Macquarie University, Macquarie Business School

Multiple version iconThere are 2 versions of this paper

Date Written: March 30, 2018

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 quanti cation 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 bene ts 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 modelling, cohort features, state-space model, Bayesian inference, Markov chain Monte Carlo

JEL Classification: C11, C13, C15, C53, J11

Suggested Citation

Fung, Man Chung and Peters, Gareth and Shevchenko, Pavel V., Cohort Effects in Mortality Modelling: A Bayesian State-Space Approach (March 30, 2018). Macquarie University Faculty of Business & Economics Research Paper. Available at SSRN: https://ssrn.com/abstract=3163226 or http://dx.doi.org/10.2139/ssrn.3163226

Man Chung Fung

Commonwealth Bank of Australia

CBP
Sydney, NSW 2064
Australia

Gareth Peters

Department of Actuarial Mathematics and Statistics, Heriot-Watt University ( email )

Edinburgh Campus
Edinburgh, EH14 4AS
United Kingdom

HOME PAGE: http://garethpeters78.wixsite.com/garethwpeters

University College London - Department of Statistical Science ( email )

1-19 Torrington Place
London, WC1 7HB
United Kingdom

University of Oxford - Oxford-Man Institute of Quantitative Finance ( email )

University of Oxford Eagle House
Walton Well Road
Oxford, OX2 6ED
United Kingdom

London School of Economics & Political Science (LSE) - Systemic Risk Centre ( email )

Houghton St
London
United Kingdom

University of New South Wales (UNSW) - Faculty of Science ( email )

Australia

Macquarie University, Macquarie Business School ( email )

New South Wales 2109
Australia

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