Whatever it takes to understand a central banker: Embedding their words using neural networks

Arbeitspapier

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 utilize 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. This allows us to provide high-quality central bank-specific textual representations and demonstrate their applicability by developing an index that tracks deviations in the Fed's communication towards inflation targeting. Our findings indicate that these deviations in communication significantly impact monetary policy actions, substantially reducing the reaction towards inflation deviation in the US.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: IMFS Working Paper Series ; No. 194

Classification and Topics

• Classification: Wirtschaft ; Neural Networks and Related Topics ; Forecasting Models; Simulation Methods ; Monetary Policy ; Economic Sociology; Economic Anthropology; Language; Social and Economic Stratification
• Subject: Word Embedding ; Neural Network ; Central Bank Communication ; Natural Language Processing ; Transfer Learning

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Baumgärtner, Martin ; Zahner, Johannes
• Event: Veröffentlichung
• (who): Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS)
• (where): Frankfurt a. M.
• (when): 2023

Further information

• Handle: http://hdl.handle.net/10419/280939
• Last update: 10.03.2025, 11:42 AM CET