How to go viral: A COVID-19 model with endogenously time-varying parameters

Paul Ho, Thomas A. Lubik and Christian Matthes

Journal of Econometrics, 2023, vol. 232, issue 1, 70-86

Abstract: We estimate a panel model with endogenously time-varying parameters for COVID-19 cases and deaths in U.S. states. The functional form for infections incorporates important features of epidemiological models but is flexibly parameterized to capture different trajectories of the pandemic. Daily deaths are modeled as a spike-and-slab regression on lagged cases. Our Bayesian estimation reveals that social distancing and testing have significant effects on the parameters. For example, a 10 percentage point increase in the positive test rate is associated with a 2 percentage point increase in the death rate among reported cases. The model forecasts perform well, even relative to models from epidemiology and statistics.

Keywords: Bayesian estimation; Panel; Time-varying parameters (search for similar items in EconPapers)
JEL-codes: C32 C51 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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Working Paper: How To Go Viral: A COVID-19 Model with Endogenously Time-Varying Parameters (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:232:y:2023:i:1:p:70-86

DOI: 10.1016/j.jeconom.2021.01.001

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