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

Author

Listed:
  • Sokbae (Simon) Lee

    (Institute for Fiscal Studies and Columbia University)

  • Yuan Liao

    (Institute for Fiscal Studies)

  • Myung Hwan Seo

    (Institute for Fiscal Studies)

  • Youngki Shin

    (Institute for Fiscal Studies)

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 ?ltering method that is a variant of the Hodrick-Prescott (HP) ?lter, constrained by the number of possible kinks. We term it the sparse HP ?lter and apply it to daily data from ?ve 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 ?nd that the sparse HP ?lter provides a fewer kinks than the l1 trend ?lter, while both methods ?tting data equally well. Theo-retically, we establish risk consistency of both the sparse HP and l1 trend ?lters. Ultimately, we propose to use time-varying contact growth rates to document and monitor outbreaks of COVID-19.

Suggested Citation

  • Sokbae (Simon) Lee & Yuan Liao & Myung Hwan Seo & Youngki Shin, 2020. "Sparse HP filter: Finding kinks in the COVID-19 contact rate," CeMMAP working papers CWP32/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:32/20
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    Citations

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    Cited by:

    1. Jonas E. Arias & Jesús Fernández-Villaverde & Juan F. Rubio-Ramírez & Minchul Shin, 2021. "Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs," CESifo Working Paper Series 8977, CESifo.
    2. Jonas E. Arias & Jesús Fernández-Villaverde & Juan F. Rubio-Ramirez & Minchul Shin, 2021. "Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs," Working Papers 21-18, Federal Reserve Bank of Philadelphia.
    3. Julliard, Christian & Shi, Ran & Yuan, Kathy, 2023. "The spread of COVID-19 in London: Network effects and optimal lockdowns," Journal of Econometrics, Elsevier, vol. 235(2), pages 2125-2154.
    4. Webel, Karsten, 2022. "A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series," Discussion Papers 31/2022, Deutsche Bundesbank.
    5. Hartl, Tobias, 2021. "Monitoring the pandemic: A fractional filter for the COVID-19 contact rate," VfS Annual Conference 2021 (Virtual Conference): Climate Economics 242380, Verein für Socialpolitik / German Economic Association.
    6. Jonas E. Arias & Jesús Fernández-Villaverde & Juan Rubio Ramírez & Minchul Shin, 2021. "The Causal Effects of Lockdown Policies on Health and Macroeconomic Outcomes," NBER Working Papers 28617, National Bureau of Economic Research, Inc.
    7. Ziwei Mei & Peter C. B. Phillips & Zhentao Shi, 2022. "The boosted HP filter is more general than you might think," Papers 2209.09810, arXiv.org, revised Apr 2024.
    8. Difang Huang & Ying Liang & Boyao Wu & Yanyi Ye, 2024. "Estimating the Impact of Social Distance Policy in Mitigating COVID-19 Spread with Factor-Based Imputation Approach," Papers 2405.12180, arXiv.org.
    9. Yang, Jinyu & Dong, Dayong & Liang, Chao, 2024. "Climate policy uncertainty and the U.S. economic cycle," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    10. Saulius Jokubaitis & Dmitrij Celov, 2022. "Business Cycle Synchronization in the EU: A Regional-Sectoral Look through Soft-Clustering and Wavelet Decomposition," Papers 2206.14128, arXiv.org.
    11. Richard K. Crump & Nikolay Gospodinov & Hunter Wieman, 2023. "Sparse Trend Estimation," Staff Reports 1049, Federal Reserve Bank of New York.
    12. Soo Beom Choi & Insung Ahn, 2020. "Forecasting imported COVID-19 cases in South Korea using mobile roaming data," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-10, November.
    13. Otilia Boldea & Adriana Cornea-Madeira & João Madeira, 2023. "Disentangling the effect of measures, variants, and vaccines on SARS-CoV-2 infections in England: a dynamic intensity model," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 444-466.
    14. Tobias Hartl, 2021. "Monitoring the pandemic: A fractional filter for the COVID-19 contact rate," Papers 2102.10067, arXiv.org.
    15. Cai, Junyang & Zhou, Jian, 2022. "How many asymptomatic cases were unconfirmed in the US COVID-19 pandemic? The evidence from a serological survey," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).

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    More about this item

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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