Currently, most academic research involving the mortality modeling of multiple populations mainly focuses on factor-based approaches. Increasingly, these models are enriched with socio-economic determinants. Yet these emerging mortality models come with little attention to interpretable spatial model features. Such features could be highly valuable to demographers and old-age benefit providers in need of a comprehensive understanding of the impact of economic growth on mortality across space. To address this, we propose and investigate a family of models that extend the seminal Li-Lee factor-based stochastic mortality modeling framework to include both economic growth, as measured by the real gross domestic product (GDP), and spatial patterns of the contiguous United States mortality. Model selection performed on the introduced new class of spatial models shows that based on the AIC criteria, the introduced spatial lag of GDP with GDP (SLGG) model had the best fit. The out-of-sample forecast performance of SLGG model is shown to be more accurate than the well-known Li–Lee model. When it comes to model implications, a comparison of annuity pricing across space revealed that the SLGG model admits more regional pricing differences compared to the Li-Lee model.

中文翻译：

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空间、死亡率和经济增长

目前，大多数涉及多种人群死亡率建模的学术研究主要集中在基于因素的方法。这些模型越来越多地包含社会经济决定因素。然而，这些新兴的死亡率模型很少关注可解释的空间模型特征。这些特征对于需要全面了解经济增长对整个空间死亡率的影响的人口统计学家和老年福利提供者来说可能非常有价值。为了解决这个问题，我们提出并研究了一系列模型，这些模型扩展了开创性的基于 Li-Lee 因子的随机死亡率模型框架，以包括以实际国内生产总值 (GDP) 衡量的经济增长和邻近地区的空间格局。美国死亡率。对引入的新一类空间模型进行的模型选择表明，基于 AIC 标准，引入的 GDP 空间滞后 (SLGG) 模型具有最佳拟合效果。 SLGG 模型的样本外预测性能比著名的 Li-Lee 模型更准确。在模型含义方面，对跨空间年金定价的比较表明，与 Li-Lee 模型相比，SLGG 模型承认更多的区域定价差异。