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

Paul Haimerl and Tobias Hartl ()
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Paul Haimerl: School of Business and Economics, Maastricht University, 6200 MD Maastricht, The Netherlands
Tobias Hartl: Department of Economics and Econometrics, University of Regensburg, Universitätsstr. 31, 93053 Regensburg, Germany

Econometrics, 2023, vol. 11, issue 2, 1-15

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.

Keywords: COVID-19; regime-switching; unobserved components; Kim filter; Gibbs sampling (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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