  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.
