Coherent Cause-Specific Mortality Forecasting Via Constrained Penalized Regression Models


Por Carlo Giovanni Camarda (Institut National d’Études Démographiques - INED, France).

Cause of death data provides additional insight into the future trends of mortality, as well as provide valuable information for governments and insurance companies. Models that fit and forecast by cause of death come across several methodological problems, one of them being the inconsistency between individual estimation and forecast of mortality per cause of death and an all-cause scenario. We propose a clear-cut and fast method to obtain coherent cause-specific mortality trajectories based on Lagrange multipliers. We apply the method proposed to fit and forecast the mortality of males in the USA for the five leading causes of death.

Transmissão via Zoom.

CEAUL - Centro de Estatística e Aplicações da Universidade de Lisboa / CEMAT - Centro de Matemática Computacional e Estocástica