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Identification Of Monetary Policy Shocks From Fomc Transcripts

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  • Nataliia Ostapenko

Abstract

I propose a new approach to identifying exogenous monetary policy shocks that requires no priors on the underlying macroeconomic structure, nor any observation of monetary policy actions. My approach entails directly estimating the unexpected changes in the federal funds rate as those which cannot be predicted from the internal Federal Open Market Committee's (FOMC) discussions. I employ deep learning and basic machine learning regressors to predict the effective federal funds rate from the FOMC's discussions without imposing any time-series structure. The result of the standard three variable Structural Vector Autoregression (SVAR) with my new measure shows that economic activity and inflation decline in response to a monetary policy shock.

Suggested Citation

  • Nataliia Ostapenko, 2020. "Identification Of Monetary Policy Shocks From Fomc Transcripts," University of Tartu - Faculty of Economics and Business Administration Working Paper Series 123, Faculty of Economics and Business Administration, University of Tartu (Estonia).
    • Handle: RePEc:mtk:febawb:123
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    Cited by:

    1. Tarkom, Augustine & Ujah, Nacasius U., 2023. "Inflation, interest rate, and firm efficiency: The impact of policy uncertainty," Journal of International Money and Finance, Elsevier, vol. 131(C).

    More about this item

    Keywords

    monetary policy; identification; shock; deep learning; FOMC; transcripts;
    All these keywords.

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