Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/278518 
Year of Publication: 
2023
Series/Report no.: 
MAGKS Joint Discussion Paper Series in Economics No. 14-2023
Publisher: 
Philipps-University Marburg, School of Business and Economics, Marburg
Abstract: 
We assemble a data set of more that eight million German Twitter posts related to the war in Ukraine. Based on state-of-the-art methods of text analysis, we construct a daily index of uncertainty about the war as perceived by German Twitter. The approach also allows us to separate this index into uncertainty about sanctions against Russia, energy policy and other dimensions. We then estimate a VAR model with daily financial and macroeconomic data and identify an exogenous uncertainty shock. The increase in uncertainty has strong effects on financial markets and causes a significant decline in economic activity as well as an increase in expected inflation. We find the effects of uncertainty to be particularly strong in the first months of the war.
Subjects: 
war
Twitter
geopolitical risk
machine learning
business cycle
JEL: 
D8
E3
G1
Document Type: 
Working Paper

Files in This Item:
File
Size





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