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

Andres Garcia-Suaza, Miguel Henry (), Jesus Otero and Christopher Baum

UK Stata Conference 2023 from Stata Users Group

Abstract: We offer a two-stage (time-series and cross-section) econometric modeling approach to examine the drivers behind the spread of COVID-19 deaths across counties in the United States. 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 a function of economic and sociodemographic factors that are taken as fixed over the course of the pandemic. Here we employ the novel one-covariate-at-atime variable-selection algorithm proposed by Chudik et al. (2018) to guide the choice of regressors.

Date: 2023-09-10
New Economics Papers: this item is included in nep-hea
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http://repec.org/lsug2023/Stata_UK23_Baum.pdf

Related works:
Working Paper: Drivers of COVID-19 deaths in the United States: A two-stage modeling approach (2023)
Working Paper: Drivers of COVID-19 deaths in the United States: A two-stage modeling approach (2022)
Working Paper: Drivers of COVID-19 deaths in the United States: A two-stage modeling approach (2022)
Working Paper: Drivers of COVID-19 deaths in the United States: A two-stage modeling approach
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Persistent link: https://EconPapers.repec.org/RePEc:boc:lsug23:17

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