Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/241907
Year of Publication: 
2020
Series/Report no.: 
cemmap working paper No. CWP32/20
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
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.
Subjects: 
COVID-19
trend filtering
knots
piecewise linear fitting
Hodrick-Prescott filter
JEL: 
C51
C52
C22
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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