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.
- Sprache
-
Englisch
- Erschienen in
-
Series: MAGKS Joint Discussion Paper Series in Economics ; No. 30-2021
- Klassifikation
-
Wirtschaft
Neural Networks and Related Topics
Forecasting Models; Simulation Methods
Monetary Policy
Economic Sociology; Economic Anthropology; Language; Social and Economic Stratification
- Thema
-
Word Embedding
Neural Network
Central Bank Communication
Natural Language Processing
Transfer Learning
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Baumgärtner, Martin
Zahner, Johannes
- Ereignis
-
Veröffentlichung
- (wer)
-
Philipps-University Marburg, School of Business and Economics
- (wo)
-
Marburg
- (wann)
-
2021
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Baumgärtner, Martin
- Zahner, Johannes
- Philipps-University Marburg, School of Business and Economics
Entstanden
- 2021
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