A Bayesian Model for Age at Death with Cohort Effects

0585434 - ÚI 2025 eng V - Research Report
Dimai, M. - Brabec, Marek
A Bayesian Model for Age at Death with Cohort Effects.
Social Science Research Network, 2024. 34 s. SSRN Papers, 4763050.
Institutional support: RVO:67985807
Keywords : mortality * age at death * mixture model * cohort effects * bayesian
https://doi.org/10.2139/ssrn.4763050

BACKGROUND: Ongoing mortality trends affect the distribution of ages at death, typically described by parametric models. Cohort effects can markedly perturbate the distribution and reduce fit of such models, which must therefore account for them. OBJECTIVE: This study examines the integration of cohort effects in a three-component parametric model for the age at death distribution, applying it to data with significant cohort effects. METHODS: We employed a mixture model with a half-Normal and two Skew-Normal components, adapted into a Bayesian framework to include multiplicative cohort effects. The model was applied to data from five Italian regions, with cohort effects estimated for the 1915-1925 cohorts. RESULTS: Incorporating cohort effects significantly improved the model’s fit. A notable finding is the shift in Italy from premature to middle-age mortality components over time. CONCLUSIONS: The study underscores the importance of including cohort effects in mortality models, providing a more detailed picture of mortality trends. CONTRIBUTION: This work introduces a novel application of a Bayesian mixture model with cohort effects, offering enhanced tools for demographic analysis and new insights into the evolution of mortality components in Italy. This approach can be valuable for demographic studies in other regions as well.
Permanent Link: https://hdl.handle.net/11104/0353143