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

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

Journal of Econometrics, 2021, vol. 220, issue 1, 158-180

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)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (20)

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Related works:
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)
Working Paper: Sparse HP Filter: Finding Kinks in the COVID-19 Contact Rate (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:220:y:2021:i:1:p:158-180

DOI: 10.1016/j.jeconom.2020.08.008

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