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Gauging the Uncertainty of the Economic Outlook Using Historical Forecasting Errors : The Federal Reserve's Approach

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  • David L. Reifschneider
  • Peter Tulip

Abstract

Since November 2007, the Federal Open Market Committee (FOMC) of the U.S. Federal Reserve has regularly published participants? qualitative assessments of the uncertainty attending their individual forecasts of real activity and inflation, expressed relative to that seen on average in the past. The benchmarks used for these historical comparisons are the average root mean squared forecast errors (RMSEs) made by various private and government forecasters over the past twenty years. This paper documents how these benchmarks are constructed and discusses some of their properties. We draw several conclusions. First, if past performance is a reasonable guide to future accuracy, considerable uncertainty surrounds all macroeconomic projections, including those of FOMC participants. Second, different forecasters have similar accuracy. Third, estimates of uncertainty about future real activity and interest rates are now considerably greater than prior to the financial crisis; in contrast, estimates of inflation accuracy have changed little. Finally, fan charts-constructed as plus-or-minus one RMSE intervals about the median FOMC forecast, under the expectation that future projection errors will be unbiased and symmetrically distributed, and that the intervals cover about 70 percent of possible outcomes-provide a reasonable approximation to future uncertainty, especially when viewed in conjunction with the FOMC's qualitative assessments. That said, an assumption of symmetry about the interest rate outlook is problematic if the expected path of the federal funds rate is expected to remain low.

Suggested Citation

  • David L. Reifschneider & Peter Tulip, 2017. "Gauging the Uncertainty of the Economic Outlook Using Historical Forecasting Errors : The Federal Reserve's Approach," Finance and Economics Discussion Series 2017-020, Board of Governors of the Federal Reserve System (U.S.).
  • Handle: RePEc:fip:fedgfe:2017-20
    DOI: 10.17016/FEDS.2017.020
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    References listed on IDEAS

    as
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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Communicating Monetary Policy Uncertainty
      by Steve Cecchetti and Kim Schoenholtz in Money, Banking and Financial Markets on 2019-04-22 13:01:56

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    Cited by:

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    3. Berge, Travis J. & Chang, Andrew C. & Sinha, Nitish R., 2019. "Evaluating the conditionality of judgmental forecasts," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1627-1635.
    4. Janet L. Yellen, 2017. "Inflation, Uncertainty, and Monetary Policy : a speech at the \"Prospects for Growth: Reassessing the Fundamentals\" 59th Annual Meeting of the National Association for Business Economics, C," Speech 971, Board of Governors of the Federal Reserve System (U.S.).
    5. Galbraith, John W. & van Norden, Simon, 2019. "Asymmetry in unemployment rate forecast errors," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1613-1626.
    6. Wang, Jiayu & Quiggin, John & Wittwer, Glyn, 2019. "The rebound effect of the Australian proposed light vehicle fuel efficiency standards," Economic Analysis and Policy, Elsevier, vol. 61(C), pages 73-84.
    7. Sharpe, Steven A. & Sinha, Nitish R. & Hollrah, Christopher A., 2023. "The power of narrative sentiment in economic forecasts," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1097-1121.

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    More about this item

    Keywords

    FOMC; Fan Charts; Forecasting; Uncertainty;
    All these keywords.

    JEL classification:

    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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