136 Sparse HP Filter: Finding Kinks in the COVID-19 Contact Rate
- Sokbae Lee, Yuan Liao, Myung Hwan Seo, Youngki Shin
- sier_136.pdf
Abstract
In this paper,we estimate the time-varyingCOVID-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
JEL classification: C51; C52; C22