Bayesian mortality forecasting with overdispersion
Bayesian mortality forecasting with overdispersion
The ability to produce accurate mortality forecasts, accompanied by a set of representative uncertainty bands, is crucial in the planning of public retirement funds and various life-related businesses. In this paper, we focus on one of the drawbacks of the Poisson Lee–Carter model (Brouhns et al., 2002) that imposes mean–variance equality, restricting mortality variations across individuals. Specifically, we present two models to potentially account for overdispersion. We propose to fit these models within the Bayesian framework for various advantages, but primarily for coherency. Markov Chain Monte Carlo (MCMC) methods are implemented to carry out parameter estimation. Several comparisons are made with the Bayesian Poisson Lee–Carter model (Czado et al., 2005) to highlight the importance of accounting for overdispersion. We demonstrate that the methodology we developed prevents over-fitting and yields better calibrated prediction intervals for the purpose of mortality projections. Bridge sampling is used to approximate the marginal likelihood of each candidate model to compare the models quantitatively.
Mortality forecast, overdispersion, Bayesian methods , MCMC, Bridge Sampling
206-221
Wong, Jackie S.T.
bc6647ec-62bc-4b36-b883-2afe7c529f65
Forster, Jonathan J.
e3c534ad-fa69-42f5-b67b-11617bc84879
Smith, Peter W.F.
961a01a3-bf4c-43ca-9599-5be4fd5d3940
November 2018
Wong, Jackie S.T.
bc6647ec-62bc-4b36-b883-2afe7c529f65
Forster, Jonathan J.
e3c534ad-fa69-42f5-b67b-11617bc84879
Smith, Peter W.F.
961a01a3-bf4c-43ca-9599-5be4fd5d3940
Wong, Jackie S.T., Forster, Jonathan J. and Smith, Peter W.F.
(2018)
Bayesian mortality forecasting with overdispersion.
Insurance: Mathematics and Economics, 83, .
(doi:10.1016/j.insmatheco.2017.09.023).
Abstract
The ability to produce accurate mortality forecasts, accompanied by a set of representative uncertainty bands, is crucial in the planning of public retirement funds and various life-related businesses. In this paper, we focus on one of the drawbacks of the Poisson Lee–Carter model (Brouhns et al., 2002) that imposes mean–variance equality, restricting mortality variations across individuals. Specifically, we present two models to potentially account for overdispersion. We propose to fit these models within the Bayesian framework for various advantages, but primarily for coherency. Markov Chain Monte Carlo (MCMC) methods are implemented to carry out parameter estimation. Several comparisons are made with the Bayesian Poisson Lee–Carter model (Czado et al., 2005) to highlight the importance of accounting for overdispersion. We demonstrate that the methodology we developed prevents over-fitting and yields better calibrated prediction intervals for the purpose of mortality projections. Bridge sampling is used to approximate the marginal likelihood of each candidate model to compare the models quantitatively.
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- Accepted Manuscript
More information
Accepted/In Press date: 28 September 2017
e-pub ahead of print date: 19 October 2017
Published date: November 2018
Keywords:
Mortality forecast, overdispersion, Bayesian methods , MCMC, Bridge Sampling
Identifiers
Local EPrints ID: 416735
URI: http://eprints.soton.ac.uk/id/eprint/416735
ISSN: 0167-6687
PURE UUID: c6ecc7ab-65d2-4925-9de6-9c19cf26acb6
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Date deposited: 05 Jan 2018 17:30
Last modified: 16 Mar 2024 05:59
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Contributors
Author:
Jackie S.T. Wong
Author:
Jonathan J. Forster
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