Agree to Disagree: Measuring Hidden Dissent in FOMC Meetings

Fuente: arXiv
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Main Authors: Tsang, Kwok Ping, Yang, Zichao
Format: Preprint
Published: 2023
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author Tsang, Kwok Ping
Yang, Zichao
author_facet Tsang, Kwok Ping
Yang, Zichao
contents Using FOMC transcripts and customized deep learning models, we quantify ``hidden dissent'', or disagreement in the FOMC that is unobserved in formal votes. We find hidden dissent to be prevalent and systematically driven by macroeconomic conditions like inflation and unemployment. It strongly correlates with divergent member projections (SEP) and measures of policy sub-optimality, reflecting heterogeneity among members in policy preferences. Furthermore, we show that the financial markets respond to the hidden dissent implied in FOMC minutes.
format Preprint
id arxiv_https___arxiv_org_abs_2308_10131
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Agree to Disagree: Measuring Hidden Dissent in FOMC Meetings
Tsang, Kwok Ping
Yang, Zichao
General Economics
Economics
Using FOMC transcripts and customized deep learning models, we quantify ``hidden dissent'', or disagreement in the FOMC that is unobserved in formal votes. We find hidden dissent to be prevalent and systematically driven by macroeconomic conditions like inflation and unemployment. It strongly correlates with divergent member projections (SEP) and measures of policy sub-optimality, reflecting heterogeneity among members in policy preferences. Furthermore, we show that the financial markets respond to the hidden dissent implied in FOMC minutes.
title Agree to Disagree: Measuring Hidden Dissent in FOMC Meetings
topic General Economics
Economics
url https://arxiv.org/abs/2308.10131