1. How to go viral: A COVID-19 model with endogenously time-varying parameters
- Author
-
Christian Matthes, Paul Ho, and Thomas A. Lubik
- Subjects
Economics and Econometrics ,medicine.medical_specialty ,Coronavirus disease 2019 (COVID-19) ,Parameterized complexity ,Biology ,01 natural sciences ,Article ,010104 statistics & probability ,0502 economics and business ,Pandemic ,Epidemiology ,Statistics ,medicine ,Positive test ,0101 mathematics ,Mathematics ,050205 econometrics ,Bayes estimator ,Social distance ,Applied Mathematics ,Mortality rate ,05 social sciences ,Percentage point ,Bayesian estimation ,Regression ,Time-varying parameters ,Panel - Abstract
This paper estimates 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. The paper's 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.
- Published
- 2023