Bayesian forecasting of mortality rates using latent Gaussian models

TL;DR

This paper introduces Bayesian methods for forecasting mortality rates using latent Gaussian models and non-linear logistic models, providing accurate long-term predictions for different populations.

Contribution

It compares two Bayesian approaches for mortality forecasting, demonstrating their effectiveness and flexibility in producing detailed demographic predictions.

Findings

Both models produce reliable 21-year ahead forecasts.

Bayesian methods facilitate estimation of survivor functions and other summaries.

Models outperform some existing forecasting techniques.

Abstract

We provide forecasts for mortality rates by using two different approaches. First we employ dynamic non-linear logistic models based on 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 proposed models. 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.