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Do central banks’ forecasts take into account public opinion and views?

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  • Ricardo Nunes

Abstract

The Federal Reserve through the Federal Open Market Committee (FOMC) regularly releases macroeconomic forecasts to the general public and the US congress with the purpose of explaining the likely evolution of the economy and the appropriate stance of monetary policy. Immediately before doing so, the FOMC receives a forecast produced by the Federal Reserve staff which remains private for five years. The literature has pointed out that, despite the informational advantage of the FOMC, its forecast differs from and is not always more accurate than the staff forecast. This finding has raised concerns regarding the loss of relevant information and the usefulness of the FOMC forecasts. This paper brings evidence that the FOMC forecast also incorporates other publicly available forecasts and views, and that the weight attributed to public forecasts is larger than what is optimal given a mean squared error objective. These findings are consistent with i) the institutional role of the FOMC in being representative of a variety of public views, ii) the academic literature recommendation to use equal weights and not to overfit specific forecasts based on past performance. The statistical model can also account for several empirical regularities of the forecasts.

Suggested Citation

  • Ricardo Nunes, 2013. "Do central banks’ forecasts take into account public opinion and views?," International Finance Discussion Papers 1080, Board of Governors of the Federal Reserve System (U.S.).
  • Handle: RePEc:fip:fedgif:1080
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    References listed on IDEAS

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    1. Marcet, Albert & Sargent, Thomas J., 1989. "Convergence of least squares learning mechanisms in self-referential linear stochastic models," Journal of Economic Theory, Elsevier, vol. 48(2), pages 337-368, August.
    2. Faust, Jon & Wright, Jonathan H., 2009. "Comparing Greenbook and Reduced Form Forecasts Using a Large Realtime Dataset," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 468-479.
    3. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521632423, February.
    4. Tootell, Geoffrey M. B., 1999. "Whose monetary policy is it anyway?," Journal of Monetary Economics, Elsevier, vol. 43(1), pages 217-235, February.
    5. David L. Reifschneider & Peter Tulip, 2007. "Gauging the uncertainty of the economic outlook from historical forecasting errors," Finance and Economics Discussion Series 2007-60, Board of Governors of the Federal Reserve System (U.S.).
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    Cited by:

    1. El-Shagi, Makram & Giesen, Sebastian & Jung, Alexander, 2016. "Revisiting the relative forecast performances of Fed staff and private forecasters: A dynamic approach," International Journal of Forecasting, Elsevier, vol. 32(2), pages 313-323.
    2. Ericsson, Neil R., 2017. "How biased are U.S. government forecasts of the federal debt?," International Journal of Forecasting, Elsevier, vol. 33(2), pages 543-559.
    3. Stekler, Herman & Symington, Hilary, 2016. "Evaluating qualitative forecasts: The FOMC minutes, 2006–2010," International Journal of Forecasting, Elsevier, vol. 32(2), pages 559-570.
    4. Ericsson, Neil R., 2016. "Eliciting GDP forecasts from the FOMC’s minutes around the financial crisis," International Journal of Forecasting, Elsevier, vol. 32(2), pages 571-583.
    5. Pierre L. Siklos, 2017. "What has publishing inflation forecasts accomplished? Central banks and their competitors," CAMA Working Papers 2017-33, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    6. Messina, Jeffrey D. & Sinclair, Tara M. & Stekler, Herman, 2015. "What can we learn from revisions to the Greenbook forecasts?," Journal of Macroeconomics, Elsevier, vol. 45(C), pages 54-62.
    7. Binder, Carola Conces & Wetzel, Samantha, 2018. "The FOMC versus the staff, revisited: When do policymakers add value?," Economics Letters, Elsevier, vol. 171(C), pages 72-75.
    8. Daniel Culbertson & Tara Sinclair, 2014. "The Failure of Forecasts in the Great Recession," Challenge, Taylor & Francis Journals, vol. 57(6), pages 34-45.
    9. Bespalova, Olga, 2020. "GDP forecasts: Informational asymmetry of the SPF and FOMC minutes," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1531-1540.
    10. Charemza, Wojciech & Díaz, Carlos & Makarova, Svetlana, 2019. "Quasi ex-ante inflation forecast uncertainty," International Journal of Forecasting, Elsevier, vol. 35(3), pages 994-1007.

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