Sparse HP filter: Finding kinks in the COVID-19 contact rate

Sokbae (Simon) Lee, Yuan Liao, Myung Hwan Seo and Youngki Shin
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Yuan Liao: Institute for Fiscal Studies

No CWP32/20, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

Abstract: 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 ?ltering method that is a variant of the Hodrick-Prescott (HP) ?lter, constrained by the number of possible kinks. We term it the sparse HP ?lter and apply it to daily data from ?ve 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 ?nd that the sparse HP ?lter provides a fewer kinks than the l1 trend ?lter, while both methods ?tting data equally well. Theo-retically, we establish risk consistency of both the sparse HP and l1 trend ?lters. Ultimately, we propose to use time-varying contact growth rates to document and monitor outbreaks of COVID-19.

Date: 2020-06-24
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Sparse HP filter: Finding kinks in the COVID-19 contact rate (2021)
Working Paper: Sparse HP Filter: Finding Kinks in the COVID-19 Contact Rate (2020)
Working Paper: Sparse HP Filter: Finding Kinks in the COVID-19 Contact Rate (2020)
Working Paper: Sparse HP Filter: Finding Kinks in the COVID-19 Contact Rate (2020)
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