Multipopulation mortality modelling and forecasting: The multivariate functional principal component with time weightings approaches
This work addresses mortality forecasting for closely related populations, offering improved accuracy for demographers and policymakers, though it is incremental as it builds on existing functional data techniques.
The paper tackles joint mortality modeling and forecasting for multiple subpopulations by introducing two new models based on multivariate functional principal component analysis, with the second model outperforming existing methods in forecast accuracy when tested on sex-specific mortality data from ten developed countries.
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.