Threats to central bank independence: High-frequency identification with twitter

Francesco Bianchi, Roberto Gómez-Cram, Thilo Kind and Howard Kung

Journal of Monetary Economics, 2023, vol. 135, issue C, 37-54

Abstract: A high-frequency approach is used to analyze the effects of President Trump’s tweets that criticize the Federal Reserve on financial markets. Identification exploits a short time window around the precise timestamp for each tweet. The average effect on the expected fed funds rate is negative and statistically significant, with the magnitude growing by horizon. The tweets also lead to an increase in stock prices and to a decrease in long-term U.S. Treasury yields. VAR evidence shows that the tweets had an important impact on actual monetary policy, the stock market, bond premia, and the macroeconomy.

Keywords: High-frequency identification; Social media; Asset prices; Fed funds rate; Central bank independence (search for similar items in EconPapers)
JEL-codes: D72 E40 E50 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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Related works:
Working Paper: Threats to Central Bank Independence: High-Frequency Identification with Twitter (2019)
Working Paper: Threats to Central Bank Independence: High-Frequency Identification with Twitter (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:moneco:v:135:y:2023:i:c:p:37-54

DOI: 10.1016/j.jmoneco.2023.01.001

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