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

Han Lin Shang*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    35 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Pages (from-to)1639-1672
    Number of pages34
    JournalAnnals of Applied Statistics
    Volume10
    Issue number3
    DOIs
    Publication statusPublished - Sept 2016

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