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

In: Machine learning in central banking

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  • 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.
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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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    References listed on IDEAS

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    1. Edison, Hali J. & Marquez, Jaime, 1998. "US monetary policy and econometric modeling: tales from the FOMC transcripts 1984-1991," Economic Modelling, Elsevier, vol. 15(3), pages 411-428, July.
    2. George A. Kahn, 2012. "The Taylor Rule and the Practice of Central Banking," Book Chapters, in: Evan F. Koenig & Robert Leeson & George A. Kahn (ed.), The Taylor Rule and the Transformation of Monetary Policy, chapter 3, Hoover Institution, Stanford University.
    3. Carlo Schwarz, 2018. "ldagibbs: A command for topic modeling in Stata using latent Dirichlet allocation," Stata Journal, StataCorp LP, vol. 18(1), pages 101-117, March.
    4. Stephen Hansen & Michael McMahon & Andrea Prat, 2018. "Transparency and Deliberation Within the FOMC: A Computational Linguistics Approach," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(2), pages 801-870.
    5. Ellen E. Meade & Daniel L. Thornton, 2012. "The Phillips curve and US monetary policy: what the FOMC transcripts tell us," Oxford Economic Papers, Oxford University Press, vol. 64(2), pages 197-216, April.
    6. Hartmann, Philipp & Smets, Frank, 2018. "The first twenty years of the European Central Bank: monetary policy," Working Paper Series 2219, European Central Bank.
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    Cited by:

    1. 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).
    2. 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).
    3. 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.
    4. 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.

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    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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