This paper considers the problem of forecasting mortality rates. A large number of models have already been proposed for this task, but they generally have the disadvantage of either estimating the model in a two-step process, possibly losing efficiency, or relying on methods that are cumbersome for the practitioner to use. We instead propose using variational inference and the probabilistic programming library Pyro for estimating the model. This allows for flexibility in modelling assumptions while still being able to estimate the full model in one step. The models are fitted on Swedish mortality data and we find that the in-sample fit is good and that the forecasting performance is better than other popular models. Code is available at https://github.com/LPAndersson/VImortality.

翻译：本文研究死亡率预测问题。尽管已有大量模型被提出用于该任务，但这些方法普遍存在缺陷：要么采用两步法估计模型导致效率损失，要么依赖对实践者而言过于复杂的计算方式。我们提出使用变分推断方法并借助概率编程库Pyro进行模型估计，该方法既能灵活调整建模假设，又可实现单步完整模型估计。基于瑞典死亡率数据的实验表明，该模型不仅样本内拟合效果良好，其预测性能也优于其他主流模型。相关代码已公开于https://github.com/LPAndersson/VImortality。