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A New Mortality Framework to Identify Trends and Structural Changes in Mortality Improvement and Its Application in Forecasting

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  • Wanying Fu

    (Mathematics Department, Lebanon Valley College, Annville, PA 17003, USA)

  • Barry R. Smith

    (Mathematics Department, Lebanon Valley College, Annville, PA 17003, USA)

  • Patrick Brewer

    (Mathematics Department, Lebanon Valley College, Annville, PA 17003, USA)

  • Sean Droms

    (Mathematics Department, Lebanon Valley College, Annville, PA 17003, USA)

Abstract

We construct a new age-specific mortality framework and implement an exemplar (DLGC) that provides an excellent fit to data from various countries and across long time periods while also providing accurate mortality forecasts by projecting parameters with ARIMA models. The model parameters have clear and reasonable interpretations that, after fitting, show stable time trends that react to major world mortality events. These trends are similar for countries with similar life-expectancies and capture mortality improvement, mortality structural change, and mortality compression over time. The parameter time plots can also be used to improve forecasting accuracy by suggesting training data periods and appropriate stochastic assumptions for parameters over time. We also give a quantitative analysis on what factors contribute to increased life expectancy and gender mortality differences during different age periods.

Suggested Citation

  • Wanying Fu & Barry R. Smith & Patrick Brewer & Sean Droms, 2022. "A New Mortality Framework to Identify Trends and Structural Changes in Mortality Improvement and Its Application in Forecasting," Risks, MDPI, vol. 10(8), pages 1-38, August.
    • Handle: RePEc:gam:jrisks:v:10:y:2022:i:8:p:161-:d:884562
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    References listed on IDEAS

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