Bayesian forecasting of mortality rates by using latent Gaussian models

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.

Text

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

Available under License Creative Commons Attribution Non-commercial.

Download (910kB)