Text data analysis using Latent Dirichlet Allocation: an application to FOMC transcripts
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DOI: 10.1080/13504851.2020.1730748
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- 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.
References listed on IDEAS
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Cited by:
- 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.
- Leonardo Bursztyn & Aakaash Rao & Christopher Roth & David Yanagizawa-Drott, 2022. "Opinions as Facts," ECONtribute Discussion Papers Series 159, University of Bonn and University of Cologne, Germany.
- 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).
- 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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