Discussion Paper Details

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Title: Identifying Monetary Policy Shocks: A Natural Language Approach

Author(s): Boragan Aruoba and Thomas Drechsel

Publication Date: March 2022

Keyword(s): Federal Reserve, Machine Learning, monetary policy and Natural Language Processing

Programme Area(s): International Macroeconomics and Finance and Monetary Economics and Fluctuations

Abstract: 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.

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Bibliographic Reference

Aruoba, B and Drechsel, T. 2022. 'Identifying Monetary Policy Shocks: A Natural Language Approach'. London, Centre for Economic Policy Research.