Economic Policy Uncertainty index meets ensemble learning

Ivana Loli? (), Petar Sori? () and Marija Logaru?i? ()
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Ivana Loli?: Faculty of Economics & Business, University of Zagreb
Petar Sori?: Faculty of Economics & Business, University of Zagreb
Marija Logaru?i?: Faculty of Economics & Business, University of Zagreb
Authors registered in the RePEc Author Service: Ivana Lolić

No 11313180, Proceedings of International Academic Conferences from International Institute of Social and Economic Sciences

Abstract: We utilize two specific ensemble learning methods (ensemble linear regression model (LM) and random forest (RF)), in a data-rich environment of the Newsbank media database to scrutinize the possibilities of enhancing the predictive accuracy of Economic Policy Uncertainty (EPU) index. LM procedure mostly outperforms both RF-based assessments and the original EPU index. We find that our LM estimate behaves more like an uncertainty indicator that the RF-based uncertainty or the original EPU index. It is strongly correlated to other standard uncertainty proxies, it is more countercyclical, and it has more pronounced leading properties. Finally, we considerably widen the scope of search terms included in the calculation of EPU index. We find that the predictive precision of EPU index can be considerably increased using a more diversified set of uncertainty-related terms than the original EPU framework.

Keywords: Economic Policy Uncertainty Index; textual analysis; ensemble learning; random forest model (search for similar items in EconPapers)
JEL-codes: C55 E03 E32 (search for similar items in EconPapers)
Pages: 1 page
New Economics Papers: this item is included in nep-big and nep-mac
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Published in Proceedings of the Proceedings of the 56th International Academic Conference, Lisbon, Nov -0001, pages 38-38

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https://iises.net/proceedings/2020-international-a ... 13&iid=007&rid=13180 First version, 0000

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Journal Article: Economic Policy Uncertainty Index Meets Ensemble Learning (2022)
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