Threats to Central Bank Independence: High-Frequency Identification with Twitter

Francesco Bianchi, Thilo Kind and Howard Kung

No 26308, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: E52 E58 G1 (search for similar items in EconPapers)
Date: 2019-09
New Economics Papers: this item is included in nep-cba, nep-mac and nep-mon
Note: AP EFG ME
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Citations: View citations in EconPapers (53)

Published as Francesco Bianchi & Roberto Gómez-Cram & Thilo Kind & Howard Kung, 2023. "Threats to Central Bank Independence: High-Frequency Identification with Twitter," Journal of Monetary Economics, .

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Journal Article: Threats to central bank independence: High-frequency identification with twitter (2023)
Working Paper: Threats to Central Bank Independence: High-Frequency Identification with Twitter (2019)
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