Konferenzbeitrag
Quantification of Economic Uncertainty: a deep learning approach
Research on the measurement of uncertainty has a long tradition. Recently, the creation of the economic policy uncertainty index sparked a new wave of research on this topic. The index is based on major American newspapers with the use of manual labeling and counting of specific keywords. Several attempts of automating this procedure have been undertaken since, using Support Vector Machine and LDA analysis. The current paper takes these efforts one step further and offers an algorithm based on natural language processing and deep learning techniques for the quantification of economic policy uncertainty. The new approach allows an accurate distillation of the latent "uncertainty" underlying newspaper articles, enables an automated construction of a new index for the measurement of economic policy uncertainty, and improves on existing methods. The potential use of our new index extends to the areas of political uncertainty management, business cycle analysis, financial forecasting, and potentially, derivative pricing.
- Language
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Englisch
- Bibliographic citation
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Series: Beiträge zur Jahrestagung des Vereins für Socialpolitik 2021: Climate Economics
- Classification
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Wirtschaft
Macroeconomics and Monetary Economics: General
- Subject
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Economic Policy Uncertainty
Deep Learning
Natural Language Processing
Text Data
Forecasting
- Event
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Geistige Schöpfung
- (who)
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Gillmann, Niels
Kim, Alisa
- Event
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Veröffentlichung
- (who)
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ZBW - Leibniz Information Centre for Economics
- (where)
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Kiel, Hamburg
- (when)
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2021
- Handle
- Last update
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10.03.2025, 11:44 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Konferenzbeitrag
Associated
- Gillmann, Niels
- Kim, Alisa
- ZBW - Leibniz Information Centre for Economics
Time of origin
- 2021
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