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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
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    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.
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