Threats to Central Bank Independence: High-Frequency Identification with Twitter
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.
We thank Svetlana Bryzgalova, Matthew Gentzkow, Simon Gilchrist, Marco Grotteria, Refet Gurkaynak, Cosmin Ilut, Annette Vissing-Jorgensen, and seminar participants at Columbia University, Johns Hopkins University, SED conference, and London Business School for helpful comments and suggestions. We thank Will Jennings from PredictIt for sharing data from the website. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
Francesco Bianchi & Roberto Gómez-Cram & Thilo Kind & Howard Kung, 2023. "Threats to Central Bank Independence: High-Frequency Identification with Twitter," Journal of Monetary Economics, .