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Word2Prices: embedding central bank communications for inflation prediction

Author

Listed:
  • Bokan, Nikola
  • Lenza, Michele
  • Araujo, Douglas
  • Comazzi, Fabio Alberto

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

Suggested Citation

  • Bokan, Nikola & Lenza, Michele & Araujo, Douglas & Comazzi, Fabio Alberto, 2025. "Word2Prices: embedding central bank communications for inflation prediction," Working Paper Series 3047, European Central Bank.
  • Handle: RePEc:ecb:ecbwps:20253047
    Note: 2613775
    as

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    More about this item

    Keywords

    central bank texts; embeddings; forecasting; inflation;
    All these keywords.

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies

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