Structural Econometric Estimation of the Basic Reproduction Number for Covid-19 Across U.S. States and Selected Countries
Ida Johnsson,
Mohammad Pesaran and
Cynthia Fan Yang
Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge
Abstract:
This paper proposes a structural econometric approach to estimating the basic reproduction number (R0) of Covid-19. This approach identifies R0 in a panel regression model by filtering out the effects of mitigating factors on disease diffusion and is easy to implement. We apply the method to data from 48 contiguous U.S. states and a diverse set of countries. Our results reveal a notable concentration of R0 estimates with an average value of 4.5. Through a counterfactual analysis, we highlight a significant underestimation of the R0 when mitigating factors are not appropriately accounted for.
Keywords: basic reproduction number; Covid-19; panel threshold regression model (search for similar items in EconPapers)
JEL-codes: C13 C33 I12 I18 J18 (search for similar items in EconPapers)
Date: 2023-09-19
New Economics Papers: this item is included in nep-ecm
Note: mhp1
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://www.econ.cam.ac.uk/sites/default/files/pub ... pe-pdfs/cwpe2360.pdf
Related works:
Working Paper: Structural Econometric Estimation of the Basic Reproduction Number for Covid-19 Across U.S. States and Selected Countries (2023) 
Working Paper: Structural Econometric Estimation of the Basic Reproduction Number for Covid-19 across U.S. States and Selected Countries (2023) 
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:cam:camdae:2360
Access Statistics for this paper
More papers in Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge
Bibliographic data for series maintained by Jake Dyer ().