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Monetary policy communication shocks and the macroeconomy

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  • Goodhead, Robert
  • Kolb, Benedikt

Abstract

Using federal funds futures data, we show the importance of surprise communication as a component of monetary policy for U.S. macro variables, both before and after 2008. While Gürkaynak et al. (2005) stress the importance of monetary policy communication for asset prices, much of the subsequent VAR literature attributes all effects of monetary policy on macro variables to surprise changes in the policy rate. Instead, we distinguish between monetary policy action and "communication shocks" (surprise announcements about future policy moves), both orthogonal to internal Fed information. To do so,we use a decomposition of futures price movements exploiting variation across contract maturities. In a monthly sample from 1994 to 2008, our results indicate that it is mainly communication shocks - as opposed to actual rate-change surprises - that affect production in the ways traditionally associated with monetary policy shocks.We also use Eurodollar futures to cover the zero-lower bound period and find strong effects on inflation for long-horizon communication shocks.

Suggested Citation

  • Goodhead, Robert & Kolb, Benedikt, 2018. "Monetary policy communication shocks and the macroeconomy," Discussion Papers 46/2018, Deutsche Bundesbank.
  • Handle: RePEc:zbw:bubdps:462018
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    References listed on IDEAS

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    1. Christiano, Lawrence J & Eichenbaum, Martin & Evans, Charles, 1996. "The Effects of Monetary Policy Shocks: Evidence from the Flow of Funds," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 16-34, February.
    2. Thapar, Aditi, 2008. "Using private forecasts to estimate the effects of monetary policy," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 806-824, May.
    3. Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 254-259, April.
    4. Michael W. McCracken & Serena Ng, 2016. "FRED-MD: A Monthly Database for Macroeconomic Research," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 574-589, October.
    5. Emi Nakamura & Jón Steinsson, 2018. "High-Frequency Identification of Monetary Non-Neutrality: The Information Effect," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(3), pages 1283-1330.
    6. Forni, Mario & Gambetti, Luca, 2014. "Sufficient information in structural VARs," Journal of Monetary Economics, Elsevier, vol. 66(C), pages 124-136.
    7. Gelper, Sarah & Croux, Christophe, 2007. "Multivariate out-of-sample tests for Granger causality," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3319-3329, April.
    8. Christina D. Romer & David H. Romer, 2004. "A New Measure of Monetary Shocks: Derivation and Implications," American Economic Review, American Economic Association, vol. 94(4), pages 1055-1084, September.
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    Cited by:

    1. Parle, Conor, 2022. "The financial market impact of ECB monetary policy press conferences — A text based approach," European Journal of Political Economy, Elsevier, vol. 74(C).
    2. Ricardo J. Caballero & Alp Simsek, 2022. "Monetary Policy with Opinionated Markets," American Economic Review, American Economic Association, vol. 112(7), pages 2353-2392, July.

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    More about this item

    Keywords

    Federal Funds Futures; FOMC; Monetary Policy; VAR Model;
    All these keywords.

    JEL classification:

    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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