Panel Paper: Text Mining and Central Bank Communication

Friday, April 7, 2017 : 2:35 PM
Founders Hall Room 311 (George Mason University Schar School of Policy)

*Names in bold indicate Presenter

Sophia Kazinnik, University of Houston
This paper studies the effect of central bank communication on monetary policy predictability. Using modern text mining techniques, I extract the informational content from the set of monthly interest rate decision statements, published by the Bank of Israel from 2006 to 2016. I construct a dictionary based sentiment measure, and estimate a Taylor type forward looking reaction function. I examine whether the sentiment measure can provide a clear signal about the future direction of monetary policy. I find that, in combination with standard macroeconomic variables, i.e. output gap and inflation, the sentiment measure improves short-run predictability of policy interest rate.