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

Hali Edison () and Hector Carcel

No 11, Bank of Lithuania Discussion Paper Series from 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.

Keywords: FOMC; Text data analysis; Transcripts; Latent Dirichlet Allocation (search for similar items in EconPapers)
JEL-codes: D78 E52 E58 (search for similar items in EconPapers)
Pages: 14 pages
Date: 2019-04-05
New Economics Papers: this item is included in nep-big, nep-cba, nep-cmp, nep-hme, nep-mac, nep-mon and nep-pay
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Citations: View citations in EconPapers (1)

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Related works:
Chapter: Text data analysis using Latent Dirichlet Allocation: an application to FOMC transcripts (2022)
Journal Article: Text data analysis using Latent Dirichlet Allocation: an application to FOMC transcripts (2021)
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