Mathias Lindholm: Forecasting sub-population mortality using credibility theory

Abstract: The focus of the presentation is on how to forecast mortality rates for small sub-populations that are parts of a larger super-population. In this setting the assumption is that it is possible to produce reliable forecasts for the super-population, but the sub-populations may be too small or lack sufficient history to produce reliable forecasts if modelled separately. This setup is aligned with the ideas that underpin credibility theory, and in the present paper the classical credibility theory approach is extended to be able to handle the situation where future mortality rates are driven by a latent stochastic process, as is the case for, e.g., Lee-Carter type models. By using this approach it is possible to derive a simple and interpretable model agnostic predictor together with an explicit expression for the mean squared error of prediction.

The performance of the suggested sub-population credibility predictor is illustrated on simulated population data. These illustrations highlight how the credibility predictor serves as a compromise between only using a super-population model, and only using a potentially unreliable sub-population specific model.

This is based on joint work with Gabriele Pittarello, University of Copenhagen.