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Calibrating The Lee-Carter And The Poisson Lee-Carter Models Via Neural Networks

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  • Scognamiglio, Salvatore

Abstract

This paper introduces a neural network (NN) approach for fitting the Lee-Carter (LC) and the Poisson Lee-Carter model on multiple populations. We develop some NNs that replicate the structure of the individual LC models and allow their joint fitting by simultaneously analysing the mortality data of all the considered populations. The NN architecture is specifically designed to calibrate each individual model using all available information instead of using a population-specific subset of data as in the traditional estimation schemes. A large set of numerical experiments performed on all the countries of the Human Mortality Database shows the effectiveness of our approach. In particular, the resulting parameter estimates appear smooth and less sensitive to the random fluctuations often present in the mortality rates’ data, especially for low-population countries. In addition, the forecasting performance results significantly improved as well.

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  • Scognamiglio, Salvatore, 2022. "Calibrating The Lee-Carter And The Poisson Lee-Carter Models Via Neural Networks," ASTIN Bulletin, Cambridge University Press, vol. 52(2), pages 519-561, May.
    • Handle: RePEc:cup:astinb:v:52:y:2022:i:2:p:519-561_6
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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. Francesca Perla & Salvatore Scognamiglio, 2023. "Locally-coherent multi-population mortality modelling via neural networks," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 46(1), pages 157-176, June.

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