Modeling COVID-19 Infection Rates by Regime-Switching Unobserved Components Models

IF 1.1 Q3 ECONOMICS
Paul Haimerl, Tobias Hartl
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引用次数: 0

Abstract

The COVID-19 pandemic is characterized by a recurring sequence of peaks and troughs. This article proposes a regime-switching unobserved components (UC) approach to model the trend of COVID-19 infections as a function of this ebb and flow pattern. Estimated regime probabilities indicate the prevalence of either an infection up- or down-turning regime for every day of the observational period. This method provides an intuitive real-time analysis of the state of the pandemic as well as a tool for identifying structural changes ex post. We find that when applied to U.S. data, the model closely tracks regime changes caused by viral mutations, policy interventions, and public behavior.
基于状态切换未观察成分模型的COVID-19感染率建模
2019冠状病毒病大流行的特点是反复出现一系列高峰和低谷。本文提出了一种状态切换未观察成分(UC)方法,将COVID-19感染趋势作为这种潮起潮落模式的函数进行建模。估计的状态概率表明,在观察期的每一天,感染要么呈上升趋势,要么呈下降趋势。这种方法提供了对大流行状况的直观实时分析,以及确定事后结构变化的工具。我们发现,当应用于美国数据时,该模型密切跟踪由病毒突变、政策干预和公众行为引起的政权变化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Econometrics
Econometrics Economics, Econometrics and Finance-Economics and Econometrics
CiteScore
2.40
自引率
20.00%
发文量
30
审稿时长
11 weeks
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