COVID-19 over Time and across States: Predictions from a Statistical Model

Paul Ho, Thomas Lubik and Christian Matthes

Richmond Fed Economic Brief, 2020, vol. 20, issue 10

Abstract: We discuss a statistical time series model to capture and forecast the dynamics of COVID-19 in the fifty U.S. states and Washington, D.C. We design the model to replicate the typical pattern of infections during a pandemic. We rely on Bayesian methods, which provide a straightforward way to quantify the uncertainty surrounding our estimates and forecasts. In this brief, we focus on North Carolina and Washington, D.C., since they have experienced different trajectories of COVID-19 and may have different implications for the efficacy of our approach.

Keywords: Regional; and; Urban; Economics (search for similar items in EconPapers)
Date: 2020
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