Retiree mortality forecasting: A partial age-range or a full age-range model?

TL;DR

This paper compares partial and full age-range models for forecasting retiree mortality, finding that truncating data before modeling yields better short-term forecasts, but long-term predictions remain challenging.

Contribution

It provides an empirical comparison of partial versus full age-range mortality models using data from developed countries, highlighting the advantages of the partial approach for short-term forecasts.

Findings

Partial age-range models outperform full models in short-term accuracy.

Long-term forecast accuracy remains uncertain for both strategies.

Expert-based expectation methods could complement statistical models for long-term forecasts.

Abstract

An essential input of annuity pricing is the future retiree mortality. From observed age-specific mortality data, modeling and forecasting can be taken place in two routes. On the one hand, we can first truncate the available data to retiree ages and then produce mortality forecasts based on a partial age-range model. On the other hand, with all available data, we can first apply a full age-range model to produce forecasts and then truncate the mortality forecasts to retiree ages. We investigate the difference in modeling the logarithmic transformation of the central mortality rates between a partial age-range and a full age-range model, using data from mainly developed countries in the Human Mortality Database (2020). By evaluating and comparing the short-term point and interval forecast accuracies, we recommend the first strategy by truncating all available data to retiree ages and then produce mortality forecasts. However, when considering the long-term forecasts, it is unclear which strategy is better since it is more difficult to find a model and parameters that are optimal. This is a disadvantage of using methods based on time series extrapolation for long-term forecasting. Instead, an expectation approach, in which experts set a future target, could be considered, noting that this method has also had limited success in the past.