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A Neural Approach to Improve the Lee-Carter Mortality Density Forecasts

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  • Mario Marino
  • Susanna Levantesi
  • Andrea Nigri

Abstract

Several countries worldwide are experiencing a continuous increase in life expectancy, extending the challenges of life actuaries and demographers in forecasting mortality. Although several stochastic mortality models have been proposed in the literature, mortality forecasting research remains a crucial task. Recently, various research works have encouraged the use of deep learning models to extrapolate suitable patterns within mortality data. Such learning models allow achieving accurate point predictions, though uncertainty measures are also necessary to support both model estimate reliability and risk evaluation. As a new advance in mortality forecasting, we formalize the deep neural network integration within the Lee-Carter framework, as a first bridge between the deep learning and the mortality density forecasts. We test our model proposal in a numerical application considering three representative countries worldwide and for both genders, scrutinizing two different fitting periods. Exploiting the meaning of both biological reasonableness and plausibility of forecasts, as well as performance metrics, our findings confirm the suitability of deep learning models to improve the predictive capacity of the Lee-Carter model, providing more reliable mortality boundaries in the long run.

Suggested Citation

  • Mario Marino & Susanna Levantesi & Andrea Nigri, 2023. "A Neural Approach to Improve the Lee-Carter Mortality Density Forecasts," North American Actuarial Journal, Taylor & Francis Journals, vol. 27(1), pages 148-165, January.
  • Handle: RePEc:taf:uaajxx:v:27:y:2023:i:1:p:148-165
    DOI: 10.1080/10920277.2022.2050260
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    Cited by:

    1. Yang Qiao & Chou-Wen Wang & Wenjun Zhu, 2024. "Machine learning in long-term mortality forecasting," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 49(2), pages 340-362, April.
    2. Jose Garrido & Yuxiang Shang & Ran Xu, 2024. "LSTM-Based Coherent Mortality Forecasting for Developing Countries," Risks, MDPI, vol. 12(2), pages 1-24, February.

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