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Multi-population modelling and forecasting life-table death counts

Shang, H.L., Haberman, S. ORCID: 0000-0003-2269-9759 & Xu, R. (2022). Multi-population modelling and forecasting life-table death counts. Insurance: Mathematics and Economics, 106, pp. 239-253. doi: 10.1016/j.insmatheco.2022.07.002


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
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
[img] Text - Accepted Version
This document is not freely accessible until 20 January 2024 due to copyright restrictions.
Available under License Creative Commons Attribution Non-commercial No Derivatives.

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