City Research Online

Abstract

When modelling the age distribution of death counts for multiple populations, we ought to consider three features: (1) how to incorporate any possible correlation among multiple populations to improve point and interval forecast accuracy through multi-population joint modelling, (2) how to forecast age distribution of death counts so that the forecasts are non-negative and have a constrained integral. (3) how to construct a prediction interval that is well-calibrated in terms of coverage. Within the framework of compositional data analysis, we apply a log-ratio transform to transform a constrained space into an unconstrained space. We apply multivariate and multilevel functional time series methods to forecast period life table death counts in the unconstrained space. Through the inverse log-ratio transformation, the forecast period life-table death counts are obtained. Using the age-specific period life-table death counts in England & Wales and Sweden obtained from Human Mortality Database (2022), we investigate one-step-ahead to 30-step-ahead point and interval forecast accuracies of the proposed models and make our recommendations.

Publication Type:	Article
Additional Information:	© 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
Publisher Keywords:	age distribution of death counts; compositional data analysis; functional principal; component analysis; log-ratio transformation; multivariate and multilevel functional principal; component regression
Subjects:	G Geography. Anthropology. Recreation > GF Human ecology. Anthropogeography H Social Sciences > HA Statistics
Departments:	Bayes Business School > Actuarial Science & Insurance
SWORD Depositor:	Symplectic Administrator

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