Spatial patterns of mortality in the United States: A spatial filtering approach

Kyran Cupido, Petar Jevtić and Antonio Paez

Insurance: Mathematics and Economics, 2020, vol. 95, issue C, 28-38

Abstract: Within the field of spatial analysis, filtering has emerged as a powerful modelling technique to retrieve systematic patterns of geographical variation. In this work, spatial filtering is introduced in the context of modelling mortality for the first time in actuarial literature. Specifically, the objective of this research is to identify patterns in the spatial distribution of mortality rates for the sixty-five and older age group at the county level for the contiguous United States. The analysis carried out pertains to the spatial autocorrelation of these rates over time. The spatial filtering methodology is applied to uncover latent spatial patterns. Furthermore, the analysis reveals the extent to which these spatial patterns remained consistent across the years in the study. Results show the existence of spatial dependencies leading to an accompanying spatial filter. Thus, incorporating spatial filters as an exploratory tool in spatial analysis proves of considerable use when it comes to enriching traditional mortality models.

Keywords: Mortality; Spatial patterns; Spatial autocorrelation; Weights matrix; Eigenvectors; Spatial filtering (search for similar items in EconPapers)
JEL-codes: C1 C13 C36 C53 J11 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:insuma:v:95:y:2020:i:c:p:28-38

DOI: 10.1016/j.insmatheco.2020.08.003

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