Identifying Structural Breaks in Stochastic Mortality Models

In recent years the issue of life expectancy has become of upmost importance to pension providers, insurance companies and the government bodies in the developed world. Significant and consistent improvements in mortality rates and hence life expectancy have led to unprecedented increases in the cost of providing for older ages. This has resulted in an explosion of stochastic mortality models forecasting trends in mortality data in order to anticipate future life expectancy and hence quantify the costs of providing for future ageing populations. Many stochastic models of mortality rates identify linear trends in mortality rates by time, age and cohort and forecast these trends into the future using standard statistical methods. The modeling approaches used fail to capture the effects of any structural change in the trend and thus potentially produce incorrect forecasts of future mortality rates. In this paper we look at a range of leading stochastic models of mortality and test for structural breaks in the trend time series. We find that in almost all cases structural breaks in the time series are present and when allowing for these the resulting forecasts are significantly improved