Bayesian forecasting of mortality rates by using latent Gaussian models

Angelos Alexopoulos, Petros Dellaportas and Jonathan J. Forster

Journal of the Royal Statistical Society Series A, 2019, vol. 182, issue 2, 689-711

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.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssa:v:182:y:2019:i:2:p:689-711

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