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

Martin Baumgaertner () and Johannes Zahner ()
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Martin Baumgaertner: THM Business School
Johannes Zahner: Philipps-Universitaet Marburg

Authors registered in the RePEc Author Service: Martin Baumgärtner ()

MAGKS Papers on Economics from Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung)

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 enables us to provide high-quality central bank-specific text-representations and demonstrate their applicability through a variety of examples in the fields of central bank objectives, financial uncertainty, and gender bias.

Keywords: Word Embedding; Neural Network; Central Bank Communication; Natural Language Processing; Transfer Learning (search for similar items in EconPapers)
JEL-codes: C45 C53 E52 Z13 (search for similar items in EconPapers)
Pages: 48 pages
Date: 2021
New Economics Papers: this item is included in nep-ban, nep-big, nep-cba, nep-cmp, nep-isf, nep-mac and nep-mon
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Persistent link: https://EconPapers.repec.org/RePEc:mar:magkse:202130

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