Economic policy uncertainty in the euro area: an unsupervised machine learning approach

Andres Azqueta-Gavaldon, Dominik Hirschbühl, Luca Onorante and Lorena Saiz

No 2359, Working Paper Series from European Central Bank

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. JEL Classification: C80, D80, E22, E66, G18, G31

Keywords: economic policy uncertainty; Europe; machine learning; textual-data (search for similar items in EconPapers)
Date: 2020-01
New Economics Papers: this item is included in nep-big, nep-cmp, nep-eec and nep-mac
Note: 2460732
References: View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:20202359

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