Alexopoulos, A;
Dellaportas, P;
Forster, JJ;
(2019)
Bayesian forecasting of mortality rates by using latent Gaussian models.
Journal of the Royal Statistical Society: Series A (Statistics in Society)
, 182
(2)
pp. 689-711.
10.1111/rssa.12422.
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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.
| Type: | Article |
|---|---|
| Title: | Bayesian forecasting of mortality rates by using latent Gaussian models |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1111/rssa.12422 |
| Publisher version: | https://doi.org/10.1111/rssa.12422 |
| Language: | English |
| Additional information: | This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. https://creativecommons.org/licenses/by-nc/4.0/ |
| Keywords: | Actuarial science, Demography, Heligman–Pollard model, Markov random field |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10057500 |
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