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Text data analysis using Latent Dirichlet Allocation: an application to FOMC transcripts

In: Machine learning in central banking

Author

Listed:
  • Hector Carcel-Villanova

Abstract

This paper applies Latent Dirichlet Allocation (LDA), a machine learning algorithm, to analyze the transcripts of the U.S. Federal Open Market Committee (FOMC) covering the period 2003 – 2012, including 45,346 passages. The goal is to detect the evolution of the different topics discussed by the members of the FOMC. The results of this exercise show that discussions on economic modelling were dominant during the Global Financial Crisis (GFC), with an increase in discussion of the banking system in the years following the GFC. Discussions on communication gained relevance toward the end of the sample as the Federal Reserve adopted a more transparent approach. The paper suggests that LDA analysis could be further exploited by researchers at central banks and institutions to identify topic priorities in relevant documents such as FOMC transcripts.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Hector Carcel-Villanova, 2022. "Text data analysis using Latent Dirichlet Allocation: an application to FOMC transcripts," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Machine learning in central banking, volume 57, Bank for International Settlements.
    • Handle: RePEc:bis:bisifc:57-21
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    File URL: https://www.bis.org/ifc/publ/ifcb57_21.pdf
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    Cited by:

    1. is not listed on IDEAS
    2. Linas Jurkšas & Rokas Kaminskas, 2023. "ECB monetary policy communication: does it move euro area yields?," Bank of Lithuania Discussion Paper Series 29, Bank of Lithuania.
    3. Ruman, Asif M., 2023. "A Comparative Textual Study of FOMC Transcripts Through Inflation Peaks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 87(C).
    4. Kaminskas, Rokas & Jurkšas, Linas, 2024. "ECB communication sentiments: How do they relate to the economic environment and financial markets?," Journal of Economics and Business, Elsevier, vol. 131(C).
    5. Leonardo Bursztyn & Aakaash Rao & Christopher Roth & David Yanagizawa-Drott, 2023. "Opinions as Facts," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(4), pages 1832-1864.
    6. Damane Moeti, 2022. "Topic Classification of Central Bank Monetary Policy Statements: Evidence from Latent Dirichlet Allocation in Lesotho," Acta Universitatis Sapientiae, Economics and Business, Sciendo, vol. 10(1), pages 199-227, September.

    More about this item

    JEL classification:

    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • D78 - Microeconomics - - Analysis of Collective Decision-Making - - - Positive Analysis of Policy Formulation and Implementation

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