Mortality and life expectancy forecasting for a group of populations in developed countries: A robust multilevel functional data method

TL;DR

This paper introduces a robust multilevel functional data approach for forecasting age-specific mortality rates and life expectancy across multiple developed countries, effectively handling outliers and improving forecast accuracy.

Contribution

It develops a novel robust multilevel functional principal component analysis method that captures common and population-specific mortality trends, enhancing forecast robustness.

Findings

Outperforms standard methods in forecast accuracy.

More effective in the presence of outliers.

Provides reliable point and interval forecasts.

Abstract

A robust multilevel functional data method is proposed to forecast age-specific mortality rate and life expectancy for two or more populations in developed countries with high-quality vital registration systems. It uses a robust multilevel functional principal component analysis of aggregate and population-specific data to extract the common trend and population-specific residual trend among populations. This method is applied to age- and sex-specific mortality rate and life expectancy for the United Kingdom from 1922 to 2011, and its forecast accuracy is then further compared with standard multilevel functional data method. For forecasting both age-specific mortality and life expectancy, the robust multilevel functional data method produces more accurate point and interval forecasts than the standard multilevel functional data method in the presence of outliers.