Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/280939 
Year of Publication: 
2023
Series/Report no.: 
IMFS Working Paper Series No. 194
Publisher: 
Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS), Frankfurt a. M.
Abstract: 
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.
Subjects: 
Word Embedding
Neural Network
Central Bank Communication
Natural Language Processing
Transfer Learning
JEL: 
C45
C53
E52
Z13
Document Type: 
Working Paper

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