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Text Data Analysis Using Latent Dirichlet Allocation: An Application to FOMC Transcripts

Author

Listed:
  • Hali Edison

    (Williams College)

  • Hector Carcel

    (Bank of Lithuania)

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.

Suggested Citation

  • Hali Edison & Hector Carcel, 2019. "Text Data Analysis Using Latent Dirichlet Allocation: An Application to FOMC Transcripts," Bank of Lithuania Discussion Paper Series 11, Bank of Lithuania.
    • Handle: RePEc:lie:dpaper:11
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    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. 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.
    2. 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).
    3. 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

    Keywords

    FOMC; Text data analysis; Transcripts; Latent Dirichlet Allocation;
    All these keywords.

    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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