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A neural network extension of the Lee–Carter model to multiple populations

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  • Richman, Ronald
  • Wüthrich, Mario V.

Abstract

The Lee–Carter (LC) model is a basic approach to forecasting mortality rates of a single population. Although extensions of the LC model to forecasting rates for multiple populations have recently been proposed, the structure of these extended models is hard to justify and the models are often difficult to calibrate, relying on customised optimisation schemes. Based on the paradigm of representation learning, we extend the LCmodel to multiple populations using neural networks, which automatically select an optimal model structure. We fit this model to mortality rates since 1950 for all countries in the Human Mortality Database and observe that the out-of-sample forecasting performance of the model is highly competitive.

Suggested Citation

  • Richman, Ronald & Wüthrich, Mario V., 2021. "A neural network extension of the Lee–Carter model to multiple populations," Annals of Actuarial Science, Cambridge University Press, vol. 15(2), pages 346-366, July.
    • Handle: RePEc:cup:anacsi:v:15:y:2021:i:2:p:346-366_8
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    Cited by:

    1. Andrea Nigri & Susanna Levantesi & Jose Manuel Aburto, 2022. "Leveraging deep neural networks to estimate age-specific mortality from life expectancy at birth," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 47(8), pages 199-232.
    2. Basellini, Ugofilippo & Camarda, Carlo Giovanni & Booth, Heather, 2022. "Thirty years on: A review of the Lee-Carter method for forecasting mortality," SocArXiv 8u34d, Center for Open Science.
    3. Benjamin Avanzi & Greg Taylor & Melantha Wang & Bernard Wong, 2023. "Machine Learning with High-Cardinality Categorical Features in Actuarial Applications," Papers 2301.12710, arXiv.org.
    4. Thilini Dulanjali Kularatne & Jackie Li & Yanlin Shi, 2022. "Forecasting Mortality Rates with a Two-Step LASSO Based Vector Autoregressive Model," Risks, MDPI, vol. 10(11), pages 1-23, November.
    5. Mark Kiermayer & Christian Wei{ss}, 2022. "Neural calibration of hidden inhomogeneous Markov chains -- Information decompression in life insurance," Papers 2201.02397, arXiv.org.

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