Economic policy uncertainty in the euro area: an unsupervised machine learning approach
Luca Onorante () and
No 2359, Working Paper Series from European Central Bank
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. JEL Classification: C80, D80, E22, E66, G18, G31
Keywords: economic policy uncertainty; Europe; machine learning; textual-data (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:20202359
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