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Article: Space, mortality, and economic growth

TitleSpace, mortality, and economic growth
Authors
Keywordsannuity pricing
economic growth
mortality
mortality forecasting
spatial lag model
Issue Date14-Feb-2024
PublisherWiley
Citation
Journal of Forecasting, 2024, v. 43, n. 5, p. 1321-1337 How to Cite?
Abstract

Currently, most academic research involving the mortality modeling of multiple populations mainly focuses on factor-based approaches. Increasingly, these models are enriched with socio-economic determinants. Yet these emerging mortality models come with little attention to interpretable spatial model features. Such features could be highly valuable to demographers and old-age benefit providers in need of a comprehensive understanding of the impact of economic growth on mortality across space. To address this, we propose and investigate a family of models that extend the seminal Li-Lee factor-based stochastic mortality modeling framework to include both economic growth, as measured by the real gross domestic product (GDP), and spatial patterns of the contiguous United States mortality. Model selection performed on the introduced new class of spatial models shows that based on the AIC criteria, the introduced spatial lag of GDP with GDP (SLGG) model had the best fit. The out-of-sample forecast performance of SLGG model is shown to be more accurate than the well-known Li–Lee model. When it comes to model implications, a comparison of annuity pricing across space revealed that the SLGG model admits more regional pricing differences compared to the Li-Lee model.


Persistent Identifierhttp://hdl.handle.net/10722/346006
ISSN
2023 Impact Factor: 3.4
2023 SCImago Journal Rankings: 0.885

 

DC FieldValueLanguage
dc.contributor.authorCupido, Kyran-
dc.contributor.authorJevtić, Petar-
dc.contributor.authorBoonen, Tim J-
dc.date.accessioned2024-09-06T00:30:24Z-
dc.date.available2024-09-06T00:30:24Z-
dc.date.issued2024-02-14-
dc.identifier.citationJournal of Forecasting, 2024, v. 43, n. 5, p. 1321-1337-
dc.identifier.issn0277-6693-
dc.identifier.urihttp://hdl.handle.net/10722/346006-
dc.description.abstract<p>Currently, most academic research involving the mortality modeling of multiple populations mainly focuses on factor-based approaches. Increasingly, these models are enriched with socio-economic determinants. Yet these emerging mortality models come with little attention to interpretable spatial model features. Such features could be highly valuable to demographers and old-age benefit providers in need of a comprehensive understanding of the impact of economic growth on mortality across space. To address this, we propose and investigate a family of models that extend the seminal Li-Lee factor-based stochastic mortality modeling framework to include both economic growth, as measured by the real gross domestic product (GDP), and spatial patterns of the contiguous United States mortality. Model selection performed on the introduced new class of spatial models shows that based on the AIC criteria, the introduced spatial lag of GDP with GDP (SLGG) model had the best fit. The out-of-sample forecast performance of SLGG model is shown to be more accurate than the well-known Li–Lee model. When it comes to model implications, a comparison of annuity pricing across space revealed that the SLGG model admits more regional pricing differences compared to the Li-Lee model.</p>-
dc.languageeng-
dc.publisherWiley-
dc.relation.ispartofJournal of Forecasting-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectannuity pricing-
dc.subjecteconomic growth-
dc.subjectmortality-
dc.subjectmortality forecasting-
dc.subjectspatial lag model-
dc.titleSpace, mortality, and economic growth-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1002/for.3086-
dc.identifier.scopuseid_2-s2.0-85185441835-
dc.identifier.volume43-
dc.identifier.issue5-
dc.identifier.spage1321-
dc.identifier.epage1337-
dc.identifier.eissn1099-131X-
dc.identifier.issnl0277-6693-

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