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

Arbeitspapier

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: cemmap working paper ; No. CWP32/20

Klassifikation und Themen

• Klassifikation: 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
• Thema: COVID-19 ; trend filtering ; knots ; piecewise linear fitting ; Hodrick-Prescott filter

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Lee, Sokbae ; Liao, Yuan ; Seo, Myung Hwan ; Shin, Youngki
• Ereignis: Veröffentlichung
• (wer): Centre for Microdata Methods and Practice (cemmap)
• (wo): London
• (wann): 2020

Weitere Informationen

• DOI: doi:10.1920/wp.cem.2020.3220
• Handle: http://hdl.handle.net/10419/241907
• Letzte Aktualisierung: 10.03.2025, 11:44 MEZ