Two-Dimensional Kernel Smoothing of Mortality Surface: An Evaluation of Cohort Strength

Abstract

Accurate mortality forecasts are of primary interest to insurance companies, pension providers and government welfare systems due to the rapid increase in life expectancy during the past few decades. Existing mortality models in the literature tend to project future mortality rates by extracting the observed patterns in the mortality surface. Recently, patterns found in the cohort dimension have received a considerable amount of attention. However, to our best knowledge very few studies have considered an evaluation and comparison of cohort effect across different countries. Moreover, the answer to the question of how does the incorporation of cohort effect affect the forecasting performance of mortality models still remains unclear. In this paper we introduce a new way of incorporating cohort effect at the beginning of the estimation stage via the implementation of kernel smoothing techniques. Bivariate standard normal kernel density is used and we interpret cohort effect as the correlation in age and time dimensions. Based on the results from our empirical study, we compare and discuss the differences in cohort strength across a range of developed countries. Further, the fitting and forecasting results of the proposed model has been shown to outperform some well-known mortality models in the literature under a majority of circumstances.