IDEAS home Printed from https://ideas.repec.org/p/boc/dsug23/01.html
   My bibliography  Save this paper

Drivers of COVID-19 deaths in the United States: A two-stage modeling approach

Author

Listed:
  • Andrés Garcia-Suaza

    (University del Rosario)

  • Miguel Henry

    (Greylock McKinnon Associates)

  • Jesús Otero

    (University del Rosario)

  • Kit Baum

    (Boston College)

Abstract

Our empirical strategy exploits the availability of two years (January 2020 through January 2022) of daily data on the number of confirmed deaths and cases of COVID-19 in the 3,000 U.S. counties of the 48 contiguous states and the District of Columbia. In the first stage of the analysis, we use daily time-series data on COVID-19 cases and deaths to fit mixed models of deaths against lagged confirmed cases for each county. Because the resulting coefficients are county specific, they relax the homogeneity assumption that is implicit when the analysis is performed using geographically aggregated cross-section units. In the second stage of the analysis, we assume that these county estimates are functions of economic and sociodemographic factors that are taken as fixed over the course of the pandemic. Here we employ the novel one-covariate-at-a-time variable-selection algorithm proposed by Chudik et al. (2018) to guide the choice of regressors.

Suggested Citation

  • Andrés Garcia-Suaza & Miguel Henry & Jesús Otero & Kit Baum, 2023. "Drivers of COVID-19 deaths in the United States: A two-stage modeling approach," German Stata Conference 2023 01, Stata Users Group.
    • Handle: RePEc:boc:dsug23:01
    as

    Download full text from publisher

    File URL: http://repec.org/dsug2023/Baum_DEStataConf2023.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. A. Chudik & G. Kapetanios & M. Hashem Pesaran, 2018. "A One Covariate at a Time, Multiple Testing Approach to Variable Selection in High‐Dimensional Linear Regression Models," Econometrica, Econometric Society, vol. 86(4), pages 1479-1512, July.
    2. Welsch David, 2022. "The Impact of Mask Usage on COVID-19 Deaths: Evidence from US Counties Using a Quasi-Experimental Approach," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 22(1), pages 1-28, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Viet Hoang Dinh & Didier Nibbering & Benjamin Wong, 2023. "Random Subspace Local Projections," CAMA Working Papers 2023-34, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    2. Bryan T. Kelly & Asaf Manela & Alan Moreira, 2019. "Text Selection," NBER Working Papers 26517, National Bureau of Economic Research, Inc.
    3. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2021. "Economic Predictions With Big Data: The Illusion of Sparsity," Econometrica, Econometric Society, vol. 89(5), pages 2409-2437, September.
    4. Chen, Song Xi & Guo, Bin & Qiu, Yumou, 2023. "Testing and signal identification for two-sample high-dimensional covariances via multi-level thresholding," Journal of Econometrics, Elsevier, vol. 235(2), pages 1337-1354.
    5. Iregui, Ana María & Núñez, Héctor M. & Otero, Jesús, 2021. "Testing the efficiency of inflation and exchange rate forecast revisions in a changing economic environment," Journal of Economic Behavior & Organization, Elsevier, vol. 187(C), pages 290-314.
    6. Holmes, Mark J. & Otero, Jesús, 2023. "Psychological price barriers, El Niño, La Niña: New insights for the case of coffee," Journal of Commodity Markets, Elsevier, vol. 31(C).
    7. Damian Kozbur, 2020. "Analysis of Testing‐Based Forward Model Selection," Econometrica, Econometric Society, vol. 88(5), pages 2147-2173, September.
    8. Mohsen Bahmani-Oskooee & Thouraya Hadj Amor & Ridha Nouira & Christophe Rault, 2019. "Political Risk and Real Exchange Rate: What Can We Learn from Recent Developments in Panel Data Econometrics for Emerging and Developing Countries?," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(4), pages 741-762, December.
    9. Pesaran, M. Hashem & Yang, Cynthia Fan, 2020. "Econometric analysis of production networks with dominant units," Journal of Econometrics, Elsevier, vol. 219(2), pages 507-541.
    10. Liang Chen & Juan J. Dolado & Jesús Gonzalo, 2021. "Quantile Factor Models," Econometrica, Econometric Society, vol. 89(2), pages 875-910, March.
    11. Héctor M. Núñez & Jesús Otero, 2021. "A one covariate at a time, multiple testing approach to variable selection in high‐dimensional linear regression models: A replication in a narrow sense," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(6), pages 833-841, September.
    12. Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2019. "Exponent of Cross-sectional Dependence for Residuals," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 46-102, September.
    13. Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2021. "Measurement of factor strength: Theory and practice," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 587-613, August.
    14. Christis Katsouris, 2023. "High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods," Papers 2308.16192, arXiv.org.
    15. George Kapetanios & Fotis Papailias, 2018. "Big Data & Macroeconomic Nowcasting: Methodological Review," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-12, Economic Statistics Centre of Excellence (ESCoE).
    16. George Kapetanios & M. Hashem Pesaran & Simon Reese, 2018. "A Residual-based Threshold Method for Detection of Units that are Too Big to Fail in Large Factor Models," CESifo Working Paper Series 7401, CESifo.
    17. Alexander Chudik & Janet Koech & Mark Wynne, 2021. "The Heterogeneous Effects of Global and National Business Cycles on Employment in US States and Metropolitan Areas," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(2), pages 495-517, April.
    18. Ke-Li Xu & Junjie Guo, 2021. "A New Test for Multiple Predictive Regression," CAEPR Working Papers 2022-001 Classification-C, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    19. Mitchener, Kris & Richardson, Gary, 2020. "Contagion of Fear," CEPR Discussion Papers 14510, C.E.P.R. Discussion Papers.
    20. Christopher F Baum & Andrés Garcia-Suaza & Miguel Henry & Jesús Otero, 2024. "Drivers of COVID-19 in U.S. counties: A wave-level analysis," Boston College Working Papers in Economics 1067, Boston College Department of Economics.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:boc:dsug23:01. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F Baum (email available below). General contact details of provider: https://edirc.repec.org/data/stataea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.