Word2Prices: embedding central bank communications for inflation prediction

Nikola Bokan, Michele Lenza, Douglas Araujo and Fabio Alberto Comazzi

No 3047, Working Paper Series from European Central Bank

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. JEL Classification: E31, E37, E58

Keywords: central bank texts; embeddings; forecasting; inflation (search for similar items in EconPapers)
Date: 2025-04
Note: 2613775
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Working Paper: Word2Prices: embedding central bank communications for inflation prediction (2025)
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