  There has been growing interest on forecasting mortality. In this article, we
propose a novel dynamic Bayesian approach for modeling and forecasting the
age-at-death distribution, focusing on a three-components mixture of a Dirac
mass, a Gaussian distribution and a Skew-Normal distribution. According to the
specified model, the age-at-death distribution is characterized via seven
parameters corresponding to the main aspects of infant, adult and old-age
mortality. The proposed approach focuses on coherent modeling of multiple
countries, and following a Bayesian approach to inference we allow to borrow
information across populations and to shrink parameters towards a common mean
level, implicitly penalizing diverging scenarios. Dynamic modeling across years
is induced trough an hierarchical dynamic prior distribution that allows to
characterize the temporal evolution of each mortality component and to forecast
the age-at-death distribution. Empirical results on multiple countries indicate
that the proposed approach outperforms popular methods for forecasting
mortality, providing interpretable insights on the evolution of mortality.
