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CALIBRATING THE LEE-CARTER AND THE POISSON LEE-CARTER MODELS VIA NEURAL NETWORKS

Published online by Cambridge University Press:  31 March 2022

Salvatore Scognamiglio
Salvatore Scognamiglio*
Affiliation:
Department of Management and Quantitative Sciences University of Naples “Parthenope”Naples, Italy
*
E-mail: [email protected]
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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.

Keywords

Mortality modellingmulti-population mortality modellingneural networksLee–Carter modelHuman Mortality Database
Type
Research Article
Information
ASTIN Bulletin: The Journal of the IAA , Volume 52 , Issue 2 , May 2022 , pp. 519 - 561
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of The International Actuarial Association

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