Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/278434 
Year of Publication: 
2023
Series/Report no.: 
MAGKS Joint Discussion Paper Series in Economics No. 10-2023
Publisher: 
Philipps-University Marburg, School of Business and Economics, Marburg
Abstract: 
The work by Baker et al. (2016), who propose a dictionary based method and estimate the level of economic policy uncertainty (EPU) based on the occurrence of specific terms in ten leading newspapers in the USA, is among the first ones to detect the potential of text data in economic research. Following this line of research, this paper proposes automated approaches to construction of EPU indices for different countries based on newspapers' texts. First, multilingual fastText word embeddings and BERT text embeddings are used in order to define relevant EPU key words and EPU related articles, respectively. Further, multilingual conceptualized topic modeling introduced by Bianchi et al. (2021) is performed and EPU related topics are detected. It is shown that the constructed EPU indices based on fastText embeddings Granger cause the economic activity in all of the considered countries, namely Germany, Russia, and Ukraine. Also, some of the topics uncovered by multilingual conceptualized topic modeling have proved to Granger cause the economic activity in all of the considered countries.
Subjects: 
text-as-data
fastText emeddings
BERT
economic policy uncertainty
natural language processing
Document Type: 
Working Paper

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