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Abstract

Studies from many countries find that gender differences in mortality rates and life expectancy vary by country. The multipopulation Lee-Carter family of models, a widely-used methodology, decompose mortality rates into age, time, and country components, offering valuable insights into mortality trends. We delve into the interpretability of the Lee-Carter multipopulation model, elucidating its ability to capture underlying mortality patterns and project future trajectories. Moreover, we extend our analysis by incorporating machine learning techniques to model the residuals of the Lee-Carter framework. The main contribution of the paper is to introduce these techniques in the context of the multiple population mortality models. Specifically, we employ Random Forest to refine joint mortality forecasts by country, effectively capturing complex nonlinear relationships in residuals and improving predictive performance.

In this paper, we revisit these models using new statistical techniques and data sets from the Human Mortality Database. By leveraging advanced computational algorithms, we aim to enhance the accuracy of mortality rate predictions and account for residual patterns that may not be captured by the traditional Lee-Carter approach alone. Through empirical validation and comparative analyses, we demonstrate the efficacy of integrating machine learning into multiple population mortality forecasting, thereby contributing to the refinement and improvement of mortality modeling methodologies.

Publication Type:	Article
Additional Information:	This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Publisher Keywords:	multipopulation Lee-Carter models, interpretability, forecasting, machine learning
Subjects:	G Geography. Anthropology. Recreation > GF Human ecology. Anthropogeography H Social Sciences > HC Economic History and Conditions H Social Sciences > HG Finance H Social Sciences > HN Social history and conditions. Social problems. Social reform Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	Bayes Business School Bayes Business School > Faculty of Actuarial Science & Insurance
SWORD Depositor:	Symplectic Administrator

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