Sokbae Lee, Yuan Liao, Myung Hwan Seo, Youngki Shin
arXiv 18 Jun 2020 · Econometrics · publishedJournal of Econometrics (2020) · 7 citations (OpenAlex)
arXiv:2006.10555 · PDF · DOI · OpenAlex · Extracted main text
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 $\ell_1$ trend filter, while both methods fitting data equally well. Theoretically, we establish risk consistency of both the sparse HP and $\ell_1$ trend filters. Ultimately, we propose to use time-varying $contact growth rates$ to document and monitor outbreaks of COVID-19.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Hethcote, H. W (2000) The mathematics of infectious diseases | 0.811 | 4 | 2 | 100% |
| 2 | Fern'andez-Villaverde, J. and C. I. Jones (2020) Estimating and simulating a SIRD model of COVID-19 for many countries, states, and cities | 0.737 | 3 | 2 | 100% |
| 3 | Liu, L., H. R. Moon, and F. Schorfheide (2020) Panel forecasts of country-level Covid-19 infections | 0.737 | 3 | 2 | 100% |
| 4 | Acemoglu, D., V. Chernozhukov, I. Werning, and M. D. Whinston (2020) Optimal targeted lockdowns in a multi-group SIR model | 0.644 | 2 | 2 | 100% |
| 5 | Hodrick, R. J. and E. C. Prescott (1997) Postwar U.S. business cycles: An empirical investigation | 0.644 | 2 | 2 | 100% |
| 6 | Dong, E., H. Du, and L. Gardner (2020) An interactive web-based dashboard to track COVID-19 in real time | 0.644 | 2 | 2 | 100% |
| 7 | The New York Times (2020) See which states and cities have told residents to stay at home | 0.644 | 2 | 2 | 100% |
| 8 | Pindyck, R. S (2020) COVID-19 and the welfare effects of reducing contagion | 0.644 | 2 | 2 | 100% |
| 9 | Kim, S.-J., K. Koh, S. Boyd, and D. Gorinevsky (2009) $_1$ trend filtering | 0.644 | 2 | 2 | 100% |
| 10 | Kim, Y.-J., M. H. Seo, and H.-E. Yeom (2020) Estimating a breakpoint in the pattern of spread of covid-19 in south korea self | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 58 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 15pt Business Cycle Synchronization in the EU: A Regional-Sectoral Look through Soft-Clustering and Wavelet Decomposition | 0.405 | 1 | 1 |
| 2 | The boosted HP filter is more general than you might think | 0.405 | 1 | 1 |