Dynamic modeling of mortality via mixtures of skewed distribution functions

TL;DR

This paper introduces a novel Bayesian dynamic model for forecasting mortality using a mixture of skewed distributions, effectively capturing age-specific mortality patterns and improving forecast accuracy across countries.

Contribution

It proposes a new hierarchical Bayesian approach with mixture models for dynamic mortality forecasting, allowing information sharing across populations and capturing complex age-at-death distributions.

Findings

Outperforms existing mortality forecasting methods

Provides interpretable insights into mortality evolution

Successfully models multiple countries with a unified framework

Abstract

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.