A Comparison of Forecasting Mortality Models Using Resampling Methods

David Atance, Ana Debón and Eliseo Navarro
Additional contact information
David Atance: Departamento de Economía y Dirección de Empresas, Universidad de Alcalá, Pza. San Diego S/N, 28801 Alcalá de Henares, Spain
Ana Debón: Centro de Gestión de la Calidad y del Cambio, Universitat Politècnica de València, Camino de Vera, S/N, 46022 Valencia, Spain
Eliseo Navarro: Departamento de Economía y Dirección de Empresas, Universidad de Alcalá, Pza. San Diego S/N, 28801 Alcalá de Henares, Spain

Mathematics, 2020, vol. 8, issue 9, 1-21

Abstract: The accuracy of the predictions of age-specific probabilities of death is an essential objective for the insurance industry since it dramatically affects the proper valuation of their products. Currently, it is crucial to be able to accurately calculate the age-specific probabilities of death over time since insurance companies’ profits and the social security of citizens depend on human survival; therefore, forecasting dynamic life tables could have significant economic and social implications. Quantitative tools such as resampling methods are required to assess the current and future states of mortality behavior. The insurance companies that manage these life tables are attempting to establish models for evaluating the risk of insurance products to develop a proactive approach instead of using traditional reactive schemes. The main objective of this paper is to compare three mortality models to predict dynamic life tables. By using the real data of European countries from the Human Mortality Database, this study has identified the best model in terms of the prediction ability for each sex and each European country. A comparison that uses cobweb graphs leads us to the conclusion that the best model is, in general, the Lee–Carter model. Additionally, we propose a procedure that can be applied to a life table database that allows us to choose the most appropriate model for any geographical area.

Keywords: forecasting; Lee–Carter model; resampling methods; cross-validation; cobweb graph (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/2227-7390/8/9/1550/pdf (application/pdf)
https://www.mdpi.com/2227-7390/8/9/1550/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:8:y:2020:i:9:p:1550-:d:411425

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().