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Sparse HP Filter: Finding Kinks in the COVID-19 Contact Rate

Sokbae (Simon) Lee (), Yuan Liao, Myung Hwan Seo and Youngki Shin ()

Department of Economics Working Papers from McMaster University

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 filtering method that is a variant of the Hodrick-Prescott (HP) filter, constrained by the number of possible kinks. We term it the sparse HP filter 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 l1 trend filter, while both methods fitting data equally well. Theoretically, we establish risk consistency of both the sparse HP and l1 trend filters. Ultimately, we propose to use time-varying contact growth rates to document and monitor outbreaks of COVID-19.

Keywords: COVID-19; trend filtering; knots; piecewise linear fitting; Hodrick-Prescott filter (search for similar items in EconPapers)
JEL-codes: C22 C51 C52 (search for similar items in EconPapers)
Pages: 47 pages
Date: 2020-06
New Economics Papers: this item is included in nep-ore
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Related works:
Journal Article: Sparse HP filter: Finding kinks in the COVID-19 contact rate (2021) Downloads
Working Paper: Sparse HP Filter: Finding Kinks in the COVID-19 Contact Rate (2020) Downloads
Working Paper: Sparse HP Filter: Finding Kinks in the COVID-19 Contact Rate (2020) Downloads
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