Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/228237
Authors: 
Azqueta-Gavaldón, Andrés
Hirschbühl, Dominik
Onorante, Luca
Saiz, Lorena
Year of Publication: 
2020
Series/Report no.: 
ECB Working Paper No. 2359
Abstract: 
We model economic policy uncertainty (EPU) in the four largest euro area countries by applying machine learning techniques to news articles. The unsupervised machine learning algorithm used makes it possible to retrieve the individual components of overall EPU endogenously for a wide range of languages. The uncertainty indices computed from January 2000 to May 2019 capture episodes of regulatory change, trade tensions and financial stress. In an evaluation exercise, we use a structural vector autoregression model to study the relationship between different sources of uncertainty and investment in machinery and equipment as a proxy for business investment. We document strong heterogeneity and asymmetries in the relationship between investment and uncertainty across and within countries. For example, while investment in France, Italy and Spain reacts strongly to political uncertainty shocks, in Germany investment is more sensitive to trade uncertainty shocks.
Subjects: 
economic policy uncertainty
Europe
machine learning
textual-data
JEL: 
C80
D80
E22
E66
G18
G31
Persistent Identifier of the first edition: 
ISBN: 
978-92-899-4002-3
Document Type: 
Working Paper

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