Working Paper: CEPR ID: DP17133
Authors: S. Boragan Aruoba; Thomas Drechsel
Abstract: We develop a novel method for the identification of monetary policy shocks. By applying natural language processing techniques to documents that Federal Reserve staff prepare in advance of policy decisions, we capture the Fed’s information set. Using machine learning techniques, we then predict changes in the target interest rate conditional on this information set and obtain a measure ofmonetary policy shocks as the residual. We show that the documents’ text contains essential information about the economy which is not captured by numerical forecasts that the staff include in the same documents. The dynamic responses of macro variables to our monetary policy shocks are consistent with the theoreticalconsensus. Shocks constructed by only controlling for the staff forecasts imply responses of macro variables at odds with theory. We directly link these differences to the information that our procedure extracts from the text over and above information captured by the forecasts.
Keywords: Monetary Policy; Federal Reserve; Greenbook; Natural Language Processing; Machine Learning
JEL Codes: C10; E31; E32; E52; E58
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Sentiment indicators derived from text (C43) | Forecast errors in key economic variables (E37) |
| Monetary policy shocks (E39) | Economic activity (E29) |
| Monetary policy shocks (E39) | Price levels (E30) |
| Monetary policy shocks (E39) | Bond premia (G12) |
| Text-based information (Y10) | Model's explanatory power (R-squared) (C29) |
| Monetary policy shocks (E39) | Macroeconomic variables (E19) |
| Identified monetary policy shocks (E39) | Nonsystematic decisions made by FOMC (E52) |