Calibrating the Lee-Carter and the Poisson Lee-Carter models via Neural Networks

TL;DR

This paper presents a neural network method for jointly calibrating Lee-Carter models across multiple populations, improving parameter stability and forecasting accuracy over traditional methods.

Contribution

The paper introduces a neural network architecture that enables simultaneous calibration of multiple Lee-Carter models, enhancing stability and predictive performance.

Findings

Parameter estimates are smoother and less sensitive to data fluctuations.

Forecasting accuracy is significantly improved.

Method performs well across diverse countries in the Human Mortality Database.

Abstract

This paper introduces a neural network approach for fitting the Lee-Carter and the Poisson Lee-Carter model on multiple populations. We develop some neural networks that replicate the structure of the individual LC models and allow their joint fitting by analysing the mortality data of all the considered populations simultaneously. The neural network 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 (HMD) 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.