Mortality Forecast: Local or Global?

Abstract

Accurate future mortality forecasts are of fundamental importance as they ensure adequate pricing of mortality-linked insurance and financial products. Extrapolative methods are the most commonly adopted forecasting approach in the literature on projecting future mortality rates (see for example Clayton and Schifflers, 1987; Lee and Carter, 1992). There are generally two types of mortality models using the extrapolative approach. The first extracts patterns in age, time and cohort dimensions either in a deterministic fashion or a stochastic fashion (see for example Lee and Carter, 1992; Plat, 2009). The second uses non-parametric smoothing techniques to model mortality and thus has no explicit constraints placed on the model (see for example Currie, et al, 2004; Hyndman and Ullah, 2007). We argue that the main difference between the two types of models in terms of forecasting is the fact that, the former uses global information and the latter mainly uses local information. In this paper we conduct an investigation on the comparison of the forecasting performance of the two types of models using Great Britain male mortality data from 1950 to 2009. The paper assesses the accuracy of forecasts not only based on statistical measures but also take the randomness of residuals into account. A main conclusion from the study is that, local information seems to have greater predictive power over historical information so it should be given more weights in the forecasting process. 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.