TY - JOUR
T1 - Mortality and life expectancy forecasting for a group of populations in developed countries
T2 - A multilevel functional data method
AU - Shang, Han Lin
N1 - Publisher Copyright:
© Institute of Mathematical Statistics, 2016.
PY - 2016/9
Y1 - 2016/9
N2 - A multilevel functional data method is adapted for forecasting age-specific mortality for two or more populations in developed countries with high-quality vital registration systems. It uses multilevel functional principal component analysis of aggregate and population-specific data to extract the common trend and population-specific residual trend among populations. If the forecasts of population-specific residual trends do not show a long-term trend, then convergence in forecasts may be achieved. This method is first applied to age- and sex-specific data for the United Kingdom, and its forecast accuracy is then further compared with several existing methods, including independent functional data and product-ratio methods, through a multi-country comparison. The proposed method is also demonstrated by age-, sex- and state-specific data in Australia, where the convergence in forecasts can possibly be achieved by sex and state. For forecasting age-specific mortality, the multilevel functional data method is more accurate than the other coherent methods considered. For forecasting female life expectancy at birth, the multilevel functional data method is outperformed by the Bayesian method of Raftery, Lalic and Gerland [Demogr. Res. 30 (2014) 795–822]. For forecasting male life expectancy at birth, the multilevel functional data method performs better than the Bayesian methods in terms of point forecasts, but less well in terms of interval forecasts. Supplementary materials for this article are available online.
AB - A multilevel functional data method is adapted for forecasting age-specific mortality for two or more populations in developed countries with high-quality vital registration systems. It uses multilevel functional principal component analysis of aggregate and population-specific data to extract the common trend and population-specific residual trend among populations. If the forecasts of population-specific residual trends do not show a long-term trend, then convergence in forecasts may be achieved. This method is first applied to age- and sex-specific data for the United Kingdom, and its forecast accuracy is then further compared with several existing methods, including independent functional data and product-ratio methods, through a multi-country comparison. The proposed method is also demonstrated by age-, sex- and state-specific data in Australia, where the convergence in forecasts can possibly be achieved by sex and state. For forecasting age-specific mortality, the multilevel functional data method is more accurate than the other coherent methods considered. For forecasting female life expectancy at birth, the multilevel functional data method is outperformed by the Bayesian method of Raftery, Lalic and Gerland [Demogr. Res. 30 (2014) 795–822]. For forecasting male life expectancy at birth, the multilevel functional data method performs better than the Bayesian methods in terms of point forecasts, but less well in terms of interval forecasts. Supplementary materials for this article are available online.
KW - Augmented common factor method
KW - Coherent forecasts
KW - Functional time series
KW - Life expectancy forecasting
KW - Mortality forecasting
KW - Product-ratio method
UR - http://www.scopus.com/inward/record.url?scp=84990966299&partnerID=8YFLogxK
U2 - 10.1214/16-AOAS953
DO - 10.1214/16-AOAS953
M3 - Article
SN - 1932-6157
VL - 10
SP - 1639
EP - 1672
JO - Annals of Applied Statistics
JF - Annals of Applied Statistics
IS - 3
ER -