Can Tree-Based Models Improve GAPC Mortality Models’ Forecasting Accuracy?

Generalized age–period–cohort (GAPC) models are mortality models that incorporate stochasticity, which can be represented in a generalized linear or non-linear context. By fitting the data to either mortality model, one can make forecasts for the future under the extrapolation framework. Previous research indicates that tree-based machine learning (ML) methods are suitable for improving the forecasting ability of such mortality models using different training/testing time periods. However, there is no consensus about generalizing this phenomenon to the improvement of fitted/forecasted mortality rates without depending on a particular mortality model or the model’s training/testing period. Furthermore, GAPC models assume symmetry of the interaction between the features and the mortality rates. Tree-based ML methods can capture asymmetric relationships within demographic data and complement the rigid assumption of symmetry of stochastic mortality models. The objective in our study is to re-estimate the mortality rates obtained from each mortality model by applying tree-based machine learning (ML) methods within a procedure that creates a suitable environment to improve the forecasting accuracy of each GAPC model. By combining mortality models with tree-based methods, both the interpretability of the parameters of mortality models and the features used within machine learning methods can be ensured. In the application carried out in this study for Denmark and Sweden, the results show that all tree-based ML-integrated models reduced the error (root mean squared error) compared to each pure mortality model. This study shows that if the proper procedure is applied, the forecasting ability of each mortality model can be improved.