Capturing International Influences in U.S. Monetary Policy through a NLP approach

Abstract

The U.S. Federal Reserve has a statutory dual domestic mandate of price stability and full employment. In this paper, we question about the role of the international environment in shaping U.S. monetary policy decisions. In this respect, we apply natural language processing (NLP) techniques to minutes of the Federal Open Market Committee (FOMC) and construct indexes of the attention paid by U.S. monetary policymakers to the international economic and financial situation, as well as sentiment indexes. By integrating those text-based indicators into a Taylor rule, we derive various quantitative measures of the external influences on Fed decisions. Our results show that when there is a focus on international topics within the FOMC, the Fed’s monetary policy generally tends to be more accommodative than expected by a Taylor rule. This result is robust to various alternatives including a time-varying neutral interest rate or a shadow central bank interest rate.