Arbeitspapier

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

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.

Language
Englisch

Bibliographic citation
Series: cemmap working paper ; No. CWP32/20

Classification
Wirtschaft
Model Construction and Estimation
Model Evaluation, Validation, and Selection
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Subject
COVID-19
trend filtering
knots
piecewise linear fitting
Hodrick-Prescott filter

Event
Geistige Schöpfung
(who)
Lee, Sokbae
Liao, Yuan
Seo, Myung Hwan
Shin, Youngki
Event
Veröffentlichung
(who)
Centre for Microdata Methods and Practice (cemmap)
(where)
London
(when)
2020

DOI
doi:10.1920/wp.cem.2020.3220
Handle
Last update
10.03.2025, 11:44 AM CET

Data provider

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Object type

  • Arbeitspapier

Associated

  • Lee, Sokbae
  • Liao, Yuan
  • Seo, Myung Hwan
  • Shin, Youngki
  • Centre for Microdata Methods and Practice (cemmap)

Time of origin

  • 2020

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