Multi-population mortality modeling: When the data is too much and not enough

Ko-Lun Kung, Richard D. MacMinn, Weiyu Kuo and Chenghsien Jason Tsai

Insurance: Mathematics and Economics, 2022, vol. 103, issue C, 41-55

Abstract: A large number of mortality rates yield estimation issues in a mortality model. The first issue is about the consistency of factor estimates when the number of mortality rates is more than the number of observations. The second issue concerns the heterogeneity among multiple populations or within a single population. We apply the framework of the approximate factor model to resolve these issues. The empirical tests on individual and multiple populations show that incorporating idiosyncratic heteroskedasticities and correlations into estimations improves in-sample fitting and out-of-sample forecasting. By comparing with existing models, we conclude that the improvements come from capturing the heteroskedasticities and correlations in the higher-order idiosyncratic errors.

Keywords: Multi-population mortality; Approximate factor model; Idiosyncratic heteroskedasticity; Correlation; Mahalanobis distance (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:insuma:v:103:y:2022:i:c:p:41-55

DOI: 10.1016/j.insmatheco.2021.12.005

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