A Penalized Distributed Lag Non-Linear Lee-Carter Framework for Regional Weekly Mortality Forecasting

Robben, Jens;Barigou, Karim
(2025) , 32 pages

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Abstract
Accurate forecasts of weekly mortality are essential for public health and the insurance industry. We develop a forecasting framework that extends the Lee–Carter model with age- and region-specific seasonal effects and penalized distributed lag non-linear components that capture the delayed and non-linear effects of heat, cold, and influenza on mortality. The model accommodates overdispersed mortality rates via a negative binomial distribution. We model the temporal dynamics of the latent factors in the model using SARIMAX processes and capture cross-regional dependencies through a copula-based approach. Using regional French mortality data (1990–2019), we demonstrate that the proposed framework yields well-calibrated forecast distributions and improves predictive accuracy relative to benchmark models. The results further show substantial heterogeneity in temperature- and influenza-related relative risks between ages and regions. These findings underscore the importance of incorporating exogenous drivers and dependence structures into a weekly mortality forecasting framework.
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Robben, J., & Barigou, K. (2025). A Penalized Distributed Lag Non-Linear Lee-Carter Framework for Regional Weekly Mortality Forecasting (LIDAM Discussion Paper ISBA 2025/16). https://hdl.handle.net/2078.5/258472