Dependence modelling across major causes of death via time-varying copula state space models
提出时变Copula状态空间模型，利用美国2015至2022年周死亡率数据，量化COVID-19对五种主要死因的死亡率及依赖结构的影响，并基于情景预测总死亡人数。
Abstract We propose a time-varying copula state space approach, which quantifies and visualizes the joint dynamics across major causes of death, utilizing data both before and during the COVID-19 pandemic. Our research investigates how the COVID-19 pandemic has affected mortality experience of five major causes, and more importantly how COVID-19 has changed the dependence structure across these causes. This enables us to gain more insights into the potential impact of COVID-19 on future life expectancy, and conduct scenario-based projections. Based on US weekly mortality data from January 2015 to November 2022, we find that COVID-19 has elevated mortality levels for the majority of causes and altered the dependence structure across these causes, particularly for Alzheimer’s and respiratory diseases. In our scenario-based analysis, we observe a noticeably wider prediction interval for total deaths when the number of COVID-19 deaths is assumed to be high, confirming the significant impact of the pandemic on population mortality. This finding could help explain the extreme mortality levels experienced during the pandemic.