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“Making Text Talk”: The Minutes of the Central Bank of Brazil and the Real Economy

Author

Listed:
  • Carlos Moreno Pérez

    (Banco de España)

  • Marco Minozzo

    (University of Verona)

Abstract

This paper investigates the relationship between the views expressed in the minutes of the meetings of the Central Bank of Brazil’s Monetary Policy Committee (COPOM) and the real economy. It applies various computational linguistic machine learning algorithms to construct measures of the minutes of the COPOM. First, we create measures of the content of the paragraphs of the minutes using Latent Dirichlet Allocation (LDA). Second, we build an uncertainty index for the minutes using Word Embedding and K-Means. Then, we combine these indices to create two topic-uncertainty indices. The first one is constructed from paragraphs with a higher probability of topics related to “general economic conditions”. The second topic-uncertainty index is constructed from paragraphs that have a higher probability of topics related to “inflation” and the “monetary policy discussion”. Finally, we employ a structural VAR model to explore the lasting effects of these uncertainty indices on certain Brazilian macroeconomic variables. Our results show that greater uncertainty leads to a decline in inflation, the exchange rate, industrial production and retail trade in the period from January 2000 to July 2019.

Suggested Citation

  • Carlos Moreno Pérez & Marco Minozzo, 2022. "“Making Text Talk”: The Minutes of the Central Bank of Brazil and the Real Economy," Working Papers 2240, Banco de España.
    • Handle: RePEc:bde:wpaper:2240
    DOI: https://doi.org/10.53479/23646
    as

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    File URL: https://www.bde.es/f/webbde/SES/Secciones/Publicaciones/PublicacionesSeriadas/DocumentosTrabajo/22/Files/dt2240e.pdf
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    References listed on IDEAS

    as
    1. Chakraborty, Chiranjit & Joseph, Andreas, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
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    More about this item

    Keywords

    Central Bank of Brazil; monetary policy communication; Latent Dirichlet Allocation; monetary policy uncertainty; Structural Vector Autoregressive model; Word Embedding;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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