Evaluation of the forecasting accuracy of stochastic mortality models: An analysis of developed and developing countries

Abstract

This paper evaluates the accuracy performance of eight stochastic mortality models in the forecasting of the male mortality rates pertaining to different age groups and countries. The mortality datasets for three developed countries (Canada, France and Japan) and two developing countries (Taiwan and Ukraine) are employed in this study. For each country, the age range is split into three age groups – A (0–19), B (20–60) and C (61–90). The forecasting accuracy of the mortality models is evaluated using the RMSE, MAE, MPE and MAPE metrics. Mortality models with more complex specifications perform better for the age groups B and C, than for the age group A. The cohort feature is more significant for age categories B and C, especially for the developed countries where there are significant medical and health advances. From an overall perspective, the Lee-Carter, Renshaw-Haberman and Age-Period-Cohort models are superior for the age group A while the Plat model proves to be the best forecasting model for the age categories B and C. The empirical analysis concludes that the mortality patterns diverge for different age categories and countries with different development status. The occurrence of extreme mortality events also negatively affects the patterns of human mortality.

Keywords:

• Stochastic mortality models
• cohort effect
• forecasting accuracy

Data availability statement

The data that support the findings of this study are openly available on the Human Mortality Database (www.mortality.org or www.humanmortality.de).