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Bayesian forecasting of mortality rates by using latent Gaussian models

Bayesian forecasting of mortality rates by using latent Gaussian models
Bayesian forecasting of mortality rates by using latent Gaussian models

We provide forecasts for mortality rates by using two different approaches. First we employ dynamic non-linear logistic models based on the Heligman–Pollard formula. Second, we assume that the dynamics of the mortality rates can be modelled through a Gaussian Markov random field. We use efficient Bayesian methods to estimate the parameters and the latent states of the models proposed. Both methodologies are tested with past data and are used to forecast mortality rates both for large (UK and Wales) and small (New Zealand) populations up to 21 years ahead. We demonstrate that predictions for individual survivor functions and other posterior summaries of demographic and actuarial interest are readily obtained. Our results are compared with other competing forecasting methods.

Actuarial science, Demography, Heligman–Pollard model, Markov random field
0964-1998
Alexopoulos, Angelos
d9f7c0e4-e0f2-4485-9fdc-f97e67f9b868
Dellaportas, Petros
df8947f6-37ea-4e68-8967-eb43f777a5fd
Forster, Jonathan J.
e3c534ad-fa69-42f5-b67b-11617bc84879
Alexopoulos, Angelos
d9f7c0e4-e0f2-4485-9fdc-f97e67f9b868
Dellaportas, Petros
df8947f6-37ea-4e68-8967-eb43f777a5fd
Forster, Jonathan J.
e3c534ad-fa69-42f5-b67b-11617bc84879

Alexopoulos, Angelos, Dellaportas, Petros and Forster, Jonathan J. (2018) Bayesian forecasting of mortality rates by using latent Gaussian models. Journal of the Royal Statistical Society: Series A (Statistics in Society). (doi:10.1111/rssa.12422).

Record type: Article

Abstract

We provide forecasts for mortality rates by using two different approaches. First we employ dynamic non-linear logistic models based on the Heligman–Pollard formula. Second, we assume that the dynamics of the mortality rates can be modelled through a Gaussian Markov random field. We use efficient Bayesian methods to estimate the parameters and the latent states of the models proposed. Both methodologies are tested with past data and are used to forecast mortality rates both for large (UK and Wales) and small (New Zealand) populations up to 21 years ahead. We demonstrate that predictions for individual survivor functions and other posterior summaries of demographic and actuarial interest are readily obtained. Our results are compared with other competing forecasting methods.

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Alexopoulos et al 2018 Journal of the Royal Statistical Society Series A (Statistics in Society) - Version of Record
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More information

Accepted/In Press date: 1 October 2018
e-pub ahead of print date: 20 November 2018
Keywords: Actuarial science, Demography, Heligman–Pollard model, Markov random field

Identifiers

Local EPrints ID: 426656
URI: https://eprints.soton.ac.uk/id/eprint/426656
ISSN: 0964-1998
PURE UUID: 8929b6df-6aa0-4717-bab9-1f10785173d7

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Date deposited: 07 Dec 2018 18:16
Last modified: 13 Mar 2019 17:44

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