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.
