Agree to Disagree: Measuring Hidden Dissent in FOMC Meetings

Kwok Ping Tsang and Zichao Yang

Papers from arXiv.org

Abstract: Using FOMC votes and meeting transcripts from 1976-2018, we develop a deep learning model based on self-attention mechanism to quantify hidden dissent among members. Although explicit dissent is rare, we find that members often have reservations with the policy decision, and hidden dissent is mostly driven by current or predicted macroeconomic data. Additionally, hidden dissent strongly correlates with data from the Summary of Economic Projections and a measure of monetary policy sub-optimality, suggesting it reflects both divergent preferences and differing economic outlooks among members. Finally, financial markets show an immediate response to the hidden dissent disclosed through meeting minutes.

Date: 2023-08, Revised 2024-11
New Economics Papers: this item is included in nep-big, nep-cba and nep-mon
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2308.10131

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