Multi-population modelling and forecasting life-table death counts

Han Lin Shang, Steven Haberman and Ruofan Xu

Insurance: Mathematics and Economics, 2022, vol. 106, issue C, 239-253

Abstract: When modelling the age distribution of death counts for multiple populations, we should 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 and Wales and Sweden obtained from the 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.

Keywords: Age distribution of death counts; Compositional data analysis; Functional principal component analysis; Log-ratio transformation; Multivariate and multilevel functional principal component regression (search for similar items in EconPapers)
JEL-codes: C14 C32 J11 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:insuma:v:106:y:2022:i:c:p:239-253

DOI: 10.1016/j.insmatheco.2022.07.002

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