Semantics matter: An empirical study on economic policy uncertainty index

Chung-Chi Chen, Yu-Lieh Huang and Fang Yang

International Review of Economics & Finance, 2024, vol. 89, issue PA, 1286-1302

Abstract: When dealing with textual data, previous studies mainly used a keyword-based matching method to construct indices. The economic policy uncertainty (EPU) index proposed by Baker et al. (2016) is an example. In this paper, we argue that due to its neglect of semantics, such keyword matching generates excessive noise, which affects the index quality and further leads to incorrect inferences. We investigated several neural network models and selected the best-performing classifier to remove the noise caused by keyword matching. Our empirical results revealed that the de-noised EPU index is useful in predicting economic variables and generating superior out-of-sample forecasts. Furthermore, the effects of policy uncertainty shocks on core macro variables of interest are consistent with the predictions of macroeconomic theory. Because the proposed approach is a general framework, in the future all keyword matching-based indexes can be improved under the same approach.

Keywords: Economic policy uncertainty; Natural language processing; Neural network model; Text mining (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1059056023003350
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:reveco:v:89:y:2024:i:pa:p:1286-1302

DOI: 10.1016/j.iref.2023.08.015

Access Statistics for this article

International Review of Economics & Finance is currently edited by H. Beladi and C. Chen

More articles in International Review of Economics & Finance from Elsevier
Bibliographic data for series maintained by Catherine Liu ().