DP17133 Identifying Monetary Policy Shocks: A Natural Language Approach
We propose a novel method for the identification of monetary policy shocks. By applying natural language processing techniques to documents that economists at the Federal Reserve prepare for Federal Open Market Committee meetings, we capture the information set available to the committee at the time of policy decisions. Using machine learning techniques, we then predict changes in the target interest rate conditional on this information set, and obtain a measure of monetary policy shocks as the residual. An appealing feature of our procedure is that only a small fraction of interest rate changes is attributed to exogenous shocks. We find that the dynamic responses of macroeconomic variables to our identified shocks are consistent with the theoretical consensus.