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Room 3-B3-SR01, Rontgen


Accurate forecasts of mortality are crucial for the provision of pensions and elderly care services. In this talk, I will present two novel developments in the field of mortality forecasting. The first part of the talk will focus on a recently proposed relational model to forecast adult age-at-death distributions. Leveraging functional data analysis methods, the “Segmented Transformation Age-at-death Distributions” is a parsimonious and efficient approach to model and forecast adult mortality. Mortality forecasts obtained with this approach are more accurate and optimistic than those derived from the benchmark Lee-Carter approach and its variants. The second part of the talk will concentrate on the challenge of forecasting cohort mortality data. Few methods exist to forecast cohort mortality, and the state-of-the-art approach – forecasting period mortality and extracting cohort patterns from the Lexis diagonals – has several limitations. I will show how to adapt the estimation of the Lee-Carter parameters to the structure of cohort mortality data, and then propose the “Linear Lee-Carter” model to derive more reasonable and accurate forecasts of cohort mortality.


Ugofilippo Basellini is a research scientist in the Laboratory of Digital and Computational Demography at the Max Planck Institute for Demographic Research, where he chairs the Research Area on Aging and Generational Processes. Moreover, he is an associated researcher at the Institut national d’études démographiques. His main research interests are related to statistical demography, with a particular focus on mortality modelling and forecasting, lifespan inequality and formal demographic methods for the study of mortality.