(untitled) (Jurnal Matematika, Statistika dan Komputasi, 2025)
The development of mortality tables in Indonesia so far has been based on static mortality tables, which only consider the probability of death by age, even though they are applied across different years. A dynamic analysis of mortality tables, which takes into account the observation year and the birth year of individuals, becomes important for mitigating risks, particularly in life insurance and pension fund applications. Numerous methods and models for determining stochastic mortality to form dynamic mortality tables exist, but in this study, the PLAT stochastic mortality model was used to establish dynamic mortality tables because it is suitable for all age ranges, captures cohort effects, aligns with historical data, has a non-trivial (yet not overly complex) correlation structure, does not have robustness issues, and can account for parameter risk, while maintaining a relatively simple model structure. From these mortality tables, future survival probabilities for the next several years were forecasted using the ARIMA method, and the forecast range was estimated using residual bootstrap. The forecasting results show values that do not differ significantly from Indonesia’s 2023 mortality table. Additionally, the width of the confidence intervals derived using residual bootstrap was larger than the intervals without using the residual bootstrap method, particularly for younger ages. This is significant for mortality research in pension planning, as they provide interval simulations that reflect various factors during the estimation process.