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Coherent modeling of mortality patterns for age-specific subgroups

Author

Listed:
  • Giuseppe Giordano

    (University of Salerno)

  • Steven Haberman

    (City, University of London)

  • Maria Russolillo

    (University of Salerno)

Abstract

The recent actuarial literature has shown that mortality patterns and trajectories in closely related populations are similar in some respects and that small differences are unlikely to increase in the long run. The common feeling is that mortality forecasts for individual countries could be improved by taking into account the patterns from a larger group. Starting from this consideration, we apply the three-way Lee–Carter model to a group of countries, by extending the bilinear LC model to a three-way structure, which incorporates a further component in the decomposition of the log-mortality rates. From a methodological point of view, there are several issues to deal with when focusing on such kind of data. In the presence of a three-way data structure, several choices on the pretreatment of the data could affect the whole modeling process. This kind of analysis is useful to assess the source of variation in the raw mortality data, before the extraction of the rank-one components by the LC model. The proposed procedure is used to extract an ad hoc time mortality trend parameter for age-specific subgroups. The results show that the proposed strategy leads to a more coherent description of mortality for age-specific subgroups.

Suggested Citation

  • Giuseppe Giordano & Steven Haberman & Maria Russolillo, 2019. "Coherent modeling of mortality patterns for age-specific subgroups," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 42(1), pages 189-204, June.
    • Handle: RePEc:spr:decfin:v:42:y:2019:i:1:d:10.1007_s10203-019-00245-y
    DOI: 10.1007/s10203-019-00245-y
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    References listed on IDEAS

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    Cited by:

    1. Simon Schnürch & Torsten Kleinow & Ralf Korn, 2021. "Clustering-Based Extensions of the Common Age Effect Multi-Population Mortality Model," Risks, MDPI, vol. 9(3), pages 1-32, March.
    2. Francesca Perla & Salvatore Scognamiglio, 2023. "Locally-coherent multi-population mortality modelling via neural networks," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 46(1), pages 157-176, June.
    3. David Atance & Alejandro Balbás & Eliseo Navarro, 2020. "Constructing dynamic life tables with a single-factor model," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 43(2), pages 787-825, December.

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    More about this item

    Keywords

    ANOVA; Human Mortality Database; Lee–Carter model; Three-way data analysis; Tucker-3;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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