Modeling COVID-19 Infection Rates by Regime-Switching Unobserved Components Models

Abstract

"The COVID-19 pandemic is characterized by a recurring sequence of peaks and troughs. This article proposes a regime-switching unobserved components (UC) approach to model the trend of COVID-19 infections as a function of this ebb and flow pattern. Estimated regime probabilities indicate the prevalence of either an infection up- or down-turning regime for every day of the observational period. This method provides an intuitive real-time analysis of the state of the pandemic as well as a tool for identifying structural changes ex post. We find that when applied to U.S. data, the model closely tracks regime changes caused by viral mutations, policy interventions, and public behavior." (Author's abstract, IAB-Doku) ((en))

Cite article

Haimerl, P. & Hartl, T. (2023): Modeling COVID-19 Infection Rates by Regime-Switching Unobserved Components Models. In: Econometrics, Vol. 11, No. 2. DOI:10.3390/econometrics11020010