Mortality Forecasting for Multiple Populations: An Augmented Common Factor Model with a Penalized Log-Likelihood

Abstract

Recently, the topic of multi-population mortality forecasting has gained considerable attention among researchers and end-users. One of the most popular multi-population mortality models is the augmented common factor (ACF) model. In spite of its popularity, the ACF model is subject to the limitation of producing mortality forecasts with a jagged pattern rather than a smooth relationship with age. In this paper, we attempt to mitigate this problem by generalizing the work of Delwarde et al. (2007) to a multi-population setting. The generalization involves (n p + 1) smoothing parameters, one for the common trend and one for each of the n p individual populations. The smoothing parameters are determined by using an extended leave-one-out cross-validation. We illustrate the proposed extension with real mortality data. It is found that compared to the original ACF model, the proposed extension produces mortality forecasts that are more reasonable, with less jagged patterns across ages.

A Quantitative Comparison of Multiple Population Mortality Model on Some East Asian Countries and Regions

“…Although the Li-Lee model, as Kang et al [17] reveal, is one of the classical methods among the multiple population mortality models, the fluctuation of crude mortality data defects the fitting performance. erefore, graduating the crude mortality data before fitting the model is necessary.…”

Multi-population mortality modeling with Lévy processes

“…Regarding the forecasting for multiple populations Kang et al. ( 2018 ) introduced an augmented common factor model with a penalized log-likelihood. Pitt et al.…”