Bayesian mortality modelling with pandemics: a vanishing jump approach
扩展了Lee-Carter模型，引入一种逐渐消失的跳跃效应来刻画流行病期间死亡率的冲击，并用COVID-19数据验证了模型优于传统瞬态冲击模型。
Abstract This paper extends the Lee–Carter (LC) model for single- and multi-populations to account for pandemic jump effects of vanishing kind, allowing for a more comprehensive and accurate representation of mortality rates during a pandemic, characterized by a high impact at the beginning and gradually vanishing effects over subsequent periods. While the LC model is effective in capturing mortality trends, it may not always be able to account for large, unexpected jumps in mortality rates caused by pandemics or wars. Existing models allow either for transient jumps with an effect of one period only or persistent jumps. However, there is no literature on estimating mortality time series with jumps having an effect over a small number of periods, as is typically observed in pandemics. The Bayesian approach allows to quantify the uncertainty around the parameter estimates. Empirical data from the COVID-19 pandemic show the superiority of the proposed approach, compared with models with a transitory shock effect.