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A multilevel functional data method is adapted for forecasting age-specificmortality for two or more populations in developed countries with high-qualityvital registration systems. It uses multilevel functional principal componentanalysis of aggregate and population-specific data to extract the common trendand population-specific residual trend among populations. If the forecasts ofpopulation-specific residual trends do not show a long-term trend, thenconvergence 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 thenfurther compared with several existing methods, including independentfunctional data and product-ratio methods, through a multi-country comparison.The proposed method is also demonstrated by age-, sex- and state-specific datain Australia, where the convergence in forecasts can possibly be achieved bysex and state. For forecasting age-specific mortality, the multilevelfunctional data method is more accurate than the other coherent methodsconsidered. For forecasting female life expectancy at birth, the multilevelfunctional data method is outperformed by the Bayesian method of \cite{RLG14}.For forecasting male life expectancy at birth, the multilevel functional datamethod performs better than the Bayesian methods in terms of point forecasts,but less well in terms of interval forecasts. Supplementary materials for thisarticle are available online.

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