Structural Breaks in Mortality Models : An International Comparison

In recent years the issue of life expectancy has become of upmost importance to pension providers, insurance companies and 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 and forecast these trends into the future using standard statistical methods. These approaches rely on the assumption that the direction of the linear trend will not change in the future, i.e. that structural breaks in the trend do not exist or do not have a significant impact on the mortality forecasts. Recent literature has started to question this assumption. In this paper we carry out a comprehensive investigation of the presence or otherwise of structural breaks in a selection of leading mortality models using data from 30 countries and for both males and females. We aim to identify how widespread the issue of structural breaks is in these types of models and hence comment on the validity of using such time series models in forecasting mortality rates. We find that structural breaks are present in a substantial (but not majority) of cases. Specifically, we find that structural breaks are more prevalent in male data than in female data and that the introduction of additional period factors in the model leads to a reduction in the presence of structural breaks in the main period effect. Where there is a structural break present allowing for it will produce significantly different forecasts and we conclude that the use of time series extrapolative models in forecasting mortality rates must be approached with caution