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

Published online by Cambridge University Press:  28 June 2019

Ronald Richman  and
Mario V. Wüthrich
Ronald Richman*
Affiliation:
Actuarial Department, AIG South Africa, Johannesburg, Gauteng 2196, South Africa
Mario V. Wüthrich
Affiliation:
RiskLab, Department of Mathematics, ETH Zurich, 8092, Zurich, Switzerland
*
*Corresponding author. Email: ron@ronaldrichman.co.za
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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.

Keywords

Mortality forecastingLee-Carter modelmultiple populationsneural networks
Type
Paper
Information
Annals of Actuarial Science , Volume 15 , Issue 2 , July 2021 , pp. 346 - 366
Copyright
© Institute and Faculty of Actuaries 2019

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