Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/307261 
Authors: 
Year of Publication: 
2022
Citation: 
[Journal:] Journal of Spatial Econometrics [ISSN:] 2662-298X [Volume:] 3 [Article No.:] 9 [Publisher:] Springer International Publishing [Place:] Cham [Year:] 2022
Publisher: 
Springer International Publishing, Cham
Abstract: 
The Covid-19 pandemic requires a continuous evaluation of whether current policies and measures taken are sufficient to protect vulnerable populations. One quantitative indicator of policy effectiveness and pandemic severity is the case fatality ratio, which relies on the lagged number of infections relative to current deaths. The appropriate length of the time lag to be used, however, is heavily debated. In this article, I contribute to this debate by determining the temporal lag between the number of infections and deaths using daily panel data from Germany's 16 federal states. To account for the dynamic spatial spread of the virus, I rely on different spatial econometric models that allow not only to consider the infections in a given state but also spillover effects through infections in neighboring federal states. My results suggest that a wave of infections within a given state is followed by increasing death rates 12 days later. Yet, if the number of infections in other states rises, the number of death cases within that given state subsequently decreases. The results of this article contribute to the better understanding of the dynamic spatio-temporal spread of the virus in Germany, which is indispensable for the design of effective policy responses.
Subjects: 
Covid-19
Spatio-temporal models
Time lag effects
Spatial spillovers
Spatial Durbin model
Germany
JEL: 
C21
C23
I10
I18
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.