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 utilize...

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Veröffentlicht in:MAGKS - Joint Discussion Paper Series in Economics (Band 30-2021)
Autoren: Baumgärtner, Martin, Zahner, Johannes
Format: Artikel
Sprache:Englisch
Veröffentlicht: Philipps-Universität Marburg 2021
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Zusammenfassung: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.
Umfang:43 Seiten
ISSN:1867-3678
DOI:10.17192/es2024.0703