Multipopulation mortality modelling and forecasting: The multivariate functional principal component with time weightings approaches

TL;DR

This paper introduces two new multivariate functional principal component models for joint mortality forecasting of related subpopulations, demonstrating that the second model improves forecast accuracy over existing methods using data from ten countries.

Contribution

The paper develops a novel multivariate functional principal component approach for coherent mortality modeling, enhancing forecast accuracy for related populations.

Findings

The second model outperforms existing models in forecast accuracy.

The first model maintains comparable forecast ability with existing methods.

Proposed models effectively capture mortality pattern similarities among subpopulations.

Abstract

Human mortality patterns and trajectories in closely related populations are likely linked together and share similarities. It is always desirable to model them simultaneously while taking their heterogeneity into account. This paper introduces two new models for joint mortality modelling and forecasting multiple subpopulations in adaptations of the multivariate functional principal component analysis techniques. The first model extends the independent functional data model to a multi-population modelling setting. In the second one, we propose a novel multivariate functional principal component method for coherent modelling. Its design primarily fulfils the idea that when several subpopulation groups have similar socio-economic conditions or common biological characteristics, such close connections are expected to evolve in a non-diverging fashion. We demonstrate the proposed methods by using sex-specific mortality data. Their forecast performances are further compared with several existing models, including the independent functional data model and the Product-Ratio model, through comparisons with mortality data of ten developed countries. Our experiment results show that the first proposed model maintains a comparable forecast ability with the existing methods. In contrast, the second proposed model outperforms the first model as well as the current models in terms of forecast accuracy, in addition to several desirable properties.