Arbeitspapier
Whatever it takes to understand a central banker: Embedding their words using neural networks
Dictionary approaches are at the forefront of current techniques for quantifying central bank communication. This paper proposes embeddings - a language model trained using machine learning techniques - to locate words and documents in a multidimensional vector space. To accomplish this, we gather a text corpus that is unparalleled in size and diversity in the central bank communication literature, as well as introduce a novel approach to text quantification from computational linguistics. Utilizing this novel text corpus of over 23,000 documents from over 130 central banks we are able to provide high quality text-representations - embeddings - for central banks. Finally, we demonstrate the applicability of embeddings in this paper by several examples in the fields of monetary policy surprises, financial uncertainty, and gender bias.
- Language
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Englisch
- Bibliographic citation
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Series: MAGKS Joint Discussion Paper Series in Economics ; No. 30-2021
- Classification
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Wirtschaft
Neural Networks and Related Topics
Forecasting Models; Simulation Methods
Monetary Policy
Economic Sociology; Economic Anthropology; Language; Social and Economic Stratification
- Subject
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Word Embedding
Neural Network
Central Bank Communication
Natural Language Processing
Transfer Learning
- Event
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Geistige Schöpfung
- (who)
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Baumgärtner, Martin
Zahner, Johannes
- Event
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Veröffentlichung
- (who)
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Philipps-University Marburg, School of Business and Economics
- (where)
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Marburg
- (when)
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2021
- Handle
- Last update
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10.03.2025, 11:42 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the 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
- Arbeitspapier
Associated
- Baumgärtner, Martin
- Zahner, Johannes
- Philipps-University Marburg, School of Business and Economics
Time of origin
- 2021
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