Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/272263 
Erscheinungsjahr: 
2023
Schriftenreihe/Nr.: 
IMFS Working Paper Series No. 184
Verlag: 
Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS), Frankfurt a. M.
Zusammenfassung: 
We assemble a data set of more than 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.
Schlagwörter: 
war
Twitter
geopolitical risk
machine learning
business cycle
JEL: 
D8
E3
G1
Dokumentart: 
Working Paper

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