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Mortality rate forecasting: can recurrent neural networks beat the Lee-Carter model?

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  • G'abor Petneh'azi
  • J'ozsef G'all

Abstract

This article applies a long short-term memory recurrent neural network to mortality rate forecasting. The model can be trained jointly on the mortality rate history of different countries, ages, and sexes. The RNN-based method seems to outperform the popular Lee-Carter model.

Suggested Citation

  • G'abor Petneh'azi & J'ozsef G'all, 2019. "Mortality rate forecasting: can recurrent neural networks beat the Lee-Carter model?," Papers 1909.05501, arXiv.org, revised Oct 2019.
    • Handle: RePEc:arx:papers:1909.05501
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    References listed on IDEAS

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    1. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    2. Andrea Nigri & Susanna Levantesi & Mario Marino & Salvatore Scognamiglio & Francesca Perla, 2019. "A Deep Learning Integrated Lee–Carter Model," Risks, MDPI, vol. 7(1), pages 1-16, March.
    3. Hainaut, Donatien, 2018. "A Neural-Network Analyzer For Mortality Forecast," ASTIN Bulletin, Cambridge University Press, vol. 48(2), pages 481-508, May.
    4. Angus Deaton & Christina Paxson, 2001. "Mortality, Income, and Income Inequality Over time in Britain and the United States," Working Papers 267, Princeton University, Woodrow Wilson School of Public and International Affairs, Center for Health and Wellbeing..
    5. Angus S. Deaton & Christina Paxson, 2004. "Mortality, Income, and Income Inequality over Time in Britain and the United States," NBER Chapters, in: Perspectives on the Economics of Aging, pages 247-286, National Bureau of Economic Research, Inc.
    6. Hainaut, Donatien, 2018. "A Neural-Network Analyzer for Mortality Forecast," LIDAM Reprints ISBA 2018027, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    7. Paras Shah & Allon Guez, 2009. "Mortality forecasting using neural networks and an application to cause-specific data for insurance purposes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(6), pages 535-548.
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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.

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