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Drivers of COVID-19 outcomes: Evidence from a heterogeneous SAR panel-data model

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Listed:
  • Kit Baum

    (Boston College
    DIW Berlin
    CESIS)

  • Miguel Henry

    (Greylock McKinnon Associates)

Abstract

In an extension of the standard spatial autoregressive (SAR) model, Aquaro, Bailey and Pesaran (ABP; 2021, https://doi.org/10.1002/jae.2792) introduced a SAR panel model that allows one to produce heterogeneous point estimates for each spatial unit. Their methodology has been implemented as the Stata routine hetsar (Belotti, 2021, Statistical Sofware Components S458926). As the COVID-19 pandemic has evolved in the U.S. since its first outbreak in February 2020 with following resurgences of multiple widespread and severe waves of the pandemic, the level of interactions between geographic units (for example, states and counties) has differed greatly over time in terms of the prevalence of the disease. Applying ABP’s HETSAR model to 2020 and 2021 COVID-19 data outcomes (confirmed case and death rates) at the state level, we extend our previous spatial econometric analysis (Baum and Henry, 2020, Boston College Working Papers in Economics 1009) on socioeconomic and demographic factors influencing the spatial spread of COVID-19 confirmed case and death rates in the U.S.

Suggested Citation

  • Kit Baum & Miguel Henry, 2021. "Drivers of COVID-19 outcomes: Evidence from a heterogeneous SAR panel-data model," 2021 Stata Conference 34, Stata Users Group.
    • Handle: RePEc:boc:scon21:34
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