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Economic policy uncertainty in the euro area: an unsupervised machine learning approach
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| Personen und Körperschaften: | , , , |
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| Titel: | Economic policy uncertainty in the euro area: an unsupervised machine learning approach/ Andrés Azqueta-Gavaldón, Dominik Hirschbühl, Luca Onorante, Lorena Saiz |
| Format: | E-Book |
| Sprache: | Englisch |
| veröffentlicht: |
Frankfurt am Main, Germany
European Central Bank
[2020]
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| Gesamtaufnahme: |
Europäische Zentralbank: Working paper series ; no 2359 (January 2020)
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| Quelle: | Verbunddaten SWB Lizenzfreie Online-Ressourcen |
| Zusammenfassung: | 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. |
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| Umfang: | 1 Online-Ressource (circa 47 Seiten); Illustrationen |
| ISBN: |
9789289940023
9289940026 |
| DOI: | 10.2866/885526 |