Abstract
Extrapolative methods are one of the most commonly-adopted forecasting approaches in the literature on projecting future mortality rates. It can be argued that there are two types of mortality models using this approach. The first extracts patterns in age, time and cohort dimensions either in a deterministic fashion or a stochastic fashion. The second uses non-parametric smoothing techniques to model mortality and thus has no explicit constraints placed on the model. We argue that from a forecasting point of view, the main difference between the two types of models is whether they treat recent and historical information equally in the projection process. In this paper, we compare the forecasting performance of the two types of models using Great Britain male mortality data from 1950–2016. We also conduct a robustness test to see how sensitive the forecasts are to the changes in the length of historical data used to calibrate the models. The main conclusion from the study is that more recent information should be given more weight in the forecasting process as it has greater predictive power over historical information.
Highlights
Accurate future mortality forecasts are of fundamental importance as they ensure adequate pricing of mortality-linked insurance and financial products
We made a formal comparison between two sets of mortality models on their forecasting performance
The two models in each group had a similar design in their structure, but one projected future mortality rates using local information and the other global information
Summary
Accurate future mortality forecasts are of fundamental importance as they ensure adequate pricing of mortality-linked insurance and financial products. There have been a number of studies on the comparison of the forecasting performances of different models (Cairns et al 2011; Haberman and Renshaw 2009; Hyndman and Ullah 2007), and the quantitative and qualitative criteria used include: the overall accuracy; allowance for cohort effect; biological reasonableness; and the robustness of forecast. Examples of models using global information to produce mortality forecast include the well-known Lee–Carter model (Lee and Carter 1992), the Cairns–Blake–Dowd (CBD). Based on the empirical results from a multi-year-ahead backtesting exercise, we compare and comment on the differences in the forecasting performances across the two groups of models and conclude that local information is more relevant to produce accurate mortality forecast.
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