Since the Lee and Carter [57] model, many researchers have focused on forecasting age-specific mortality rates. Recent research highlights the use of mortality improvements to increase forecasting accuracy. We propose a mortality improvement model inspired by interest rate modeling, where the changes in the mortality curve depend linearly on a reduced number of age-specific or “key” age(s). These variables are directly observable and capture the general mortality trend. Previous versions used one key age, whereas we introduce a second factor to improve the explanatory and forecasting power. Across populations, the first key age is consistently around 85, while the second is near age 30. We compare the accuracy of the models with seven benchmark mortality models in six populations, including the pandemic period. The results show that including a second factor enhances performance only when it is correlated with a sufficiently wide range of age-specific rates.
