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A NEURAL-NETWORK ANALYZER FOR MORTALITY FORECAST

Published online by Cambridge University Press:  09 January 2018

Donatien Hainaut
Donatien Hainaut*
Affiliation:
Institute of Statistics, Biostatistics and Actuarial Sciences, Unversité Catholique de Louvain, Voie du Roman Pays, 30 bte L1.04.01, 1348 Louvain La Neuve, Belgium
*
E-Mail: donatien.hainaut@uclouvain.be
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Abstract

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This article proposes a neural-network approach to predict and simulate human mortality rates. This semi-parametric model is capable to detect and duplicate non-linearities observed in the evolution of log-forces of mortality. The method proceeds in two steps. During the first stage, a neural-network-based generalization of the principal component analysis summarizes the information carried by the surface of log-mortality rates in a small number of latent factors. In the second step, these latent factors are forecast with an econometric model. The term structure of log-forces of mortality is next reconstructed by an inverse transformation. The neural analyzer is adjusted to French, UK and US mortality rates, over the period 1946–2000 and validated with data from 2001 to 2014. Numerical experiments reveal that the neural approach has an excellent predictive power, compared to the Lee–Carter model with and without cohort effects.

Keywords

LongevitymortalityLee–Carterneural networkperceptron
Type
Research Article
Information
ASTIN Bulletin: The Journal of the IAA , Volume 48 , Issue 2 , May 2018 , pp. 481 - 508
Copyright
Copyright © Astin Bulletin 2018 

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