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

Francesco Bianchi, Thilo Kind and Howard Kung

No 14021, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: We use a high-frequency approach 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 the stock market, breakeven inflation, and spreads linked to the risk of financial instability. VAR evidence shows that the tweets had an important impact on actual monetary policy, the stock market, bond premia, and the macroeconomy.

Keywords: Central bank independence; Monetary policy; Twitter; Fed funds target; High-frequency identification (search for similar items in EconPapers)
JEL-codes: D72 E40 E50 (search for similar items in EconPapers)
Date: 2019-09
New Economics Papers: this item is included in nep-mac and nep-mon
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Citations: View citations in EconPapers (51)

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Related works:
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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