A well-established insight in mortality forecasting is that combining predictions from a set of models improves accuracy compared to relying on a single best model. This paper proposes a novel ensemble approach based on Shapley values, a game-theoretic measure of each model's marginal contribution to the forecast. We further compute these SHapley Additive exPlanations (SHAP)-based weights age-by-age, thereby capturing the specific contribution of each model at each age. In addition, we introduce a threshold mechanism that excludes models with negligible contributions, effectively reducing the forecast variance. Using data from 24 OECD countries, we demonstrate that our SHAP ensemble enhances out-of-sample forecasting performance, especially at longer horizons. By leveraging the complementary strengths of different mortality models and filtering out those that add little predictive power, our approach offers a robust and interpretable solution for improving mortality forecasts.

翻译：死亡率预测领域的一个成熟见解是：相较于依赖单一最优模型，整合一组模型的预测结果能显著提升预测精度。本文提出一种基于Shapley值的新型集成方法，该博弈论指标可量化每个模型对预测的边际贡献。我们进一步按年龄维度计算基于SHapley可加性解释（SHAP）的权重，从而捕捉每个模型在各年龄层的特定贡献。此外，我们引入阈值机制以排除贡献可忽略的模型，有效降低预测方差。基于24个OECD国家的数据，我们证明SHAP集成方法能显著提升样本外预测性能，尤其在长期预测范围内表现突出。通过整合不同死亡率模型的互补优势并过滤预测能力薄弱的模型，本方法为改进死亡率预测提供了兼具鲁棒性与可解释性的解决方案。