Word2Prices: Embedding Central Bank Communications for Inflation Prediction

Douglas Araujo, Nikola Bokan, Fabio Comazzi and Michele Lenza

No 19784, CEPR Discussion Papers from Centre for Economic Policy Research

Abstract: Word embeddings are vectors of real numbers associated with words, designed to capture semantic and syntactic similarity between the words in a corpus of text. We estimate the word embeddings of the European Central Bank's introductory statements at monetary policy press conferences by using a simple natural language processing model (Word2Vec), only based on the information and model parameters available as of each press conference. We show that a measure based on such embeddings contributes to improve core inflation forecasts multiple quarters ahead. Other common textual analysis techniques, such as dictionary-based metrics or sentiment metrics do not obtain the same results. The information contained in the embeddings remains valuable for out-of-sample forecasting even after controlling for the central bank inflation forecasts, which are an important input for the introductory statements.

Keywords: Inflation (search for similar items in EconPapers)
JEL-codes: E31 E37 E58 (search for similar items in EconPapers)
Date: 2024-12
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