Lee Sokbae, Liao Yuan, Seo Myung Hwan, Shin Youngki
Department of Economics, Columbia University, 420 West 118th Street, New York, NY 10027, USA.
Centre for Microdata Methods and Practice, Institute for Fiscal Studies, 7 Ridgmount Street, London WC1E 7AE, UK.
J Econom. 2021 Jan;220(1):158-180. doi: 10.1016/j.jeconom.2020.08.008. Epub 2020 Sep 26.
In this paper, we estimate the time-varying COVID-19 contact rate of a Susceptible-Infected-Recovered (SIR) model. Our measurement of the contact rate is constructed using data on actively infected, recovered and deceased cases. We propose a new trend filtering method that is a variant of the Hodrick-Prescott (HP) filter, constrained by the number of possible kinks. We term it the and apply it to daily data from five countries: Canada, China, South Korea, the UK and the US. Our new method yields the kinks that are well aligned with actual events in each country. We find that the sparse HP filter provides a fewer kinks than the trend filter, while both methods fitting data equally well. Theoretically, we establish risk consistency of both the sparse HP and trend filters. Ultimately, we propose to use time-varying to document and monitor outbreaks of COVID-19.
在本文中，我们估计了易感-感染-康复（SIR）模型中随时间变化的新冠病毒接触率。我们对接触率的测量是利用活跃感染、康复和死亡病例的数据构建的。我们提出了一种新的趋势滤波方法，它是霍德里克-普雷斯科特（HP）滤波器的一种变体，受可能的拐点数量约束。我们将其称为 并将其应用于加拿大、中国、韩国、英国和美国五个国家的每日数据。我们的新方法产生的拐点与每个国家的实际事件高度吻合。我们发现，稀疏HP滤波器提供的拐点比 趋势滤波器少，而两种方法对数据的拟合效果同样良好。从理论上讲，我们建立了稀疏HP滤波器和 趋势滤波器的风险一致性。最终，我们建议使用随时间变化的 来记录和监测新冠疫情的爆发。
