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Evaluating FOMC forecast ranges: an interval data approach

Author

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  • Henning Fischer

    () (University of Giessen)

  • Marta García-Bárzana

    () (University of Oviedo)

  • Peter Tillmann

    () (University of Giessen)

  • Peter Winker

    () (University of Giessen)

Abstract

The Federal Open Market Committee (FOMC) of the U.S. Federal Reserve publishes the range of members’ forecasts for key macroeconomic variables, but not the distribution of forecasts within this range. To evaluate these projections, previous papers compare the midpoint of the ranges with the realized outcome. This paper proposes a new approach to forecast evaluation that takes account of the interval nature of projections. It is shown that using the conventional Mincer-Zarnowitz approach to evaluate FOMC forecasts misses important information contained in the width of the forecast interval. This additional information plays a minor role at short forecast horizons but turns out to be of crucial importance for inflation and unemployment forecasts 18 months into the future. At long horizons the variation of members’ projections contains information which is more relevant for explaining future inflation than information embodied in the midpoint.

Suggested Citation

  • Henning Fischer & Marta García-Bárzana & Peter Tillmann & Peter Winker, 2012. "Evaluating FOMC forecast ranges: an interval data approach," MAGKS Papers on Economics 201213, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
  • Handle: RePEc:mar:magkse:201213
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    References listed on IDEAS

    as
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    2. Henning Fischer & Ángela Blanco‐FERNÁndez & Peter Winker, 2016. "Predicting Stock Return Volatility: Can We Benefit from Regression Models for Return Intervals?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(2), pages 113-146, March.
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    Cited by:

    1. Natsuki Arai, 2016. "Evaluating the Efficiency of the FOMC's New Economic Projections," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(5), pages 1019-1049, August.
    2. repec:ntu:ntugeo:vol2-iss1-14-054 is not listed on IDEAS
    3. Angela Blanco-Fernández & Peter Winker, 2016. "Data generation processes and statistical management of interval data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 100(4), pages 475-494, October.
    4. Gamber, Edward N. & Liebner, Jeffrey P. & Smith, Julie K., 2015. "The distribution of inflation forecast errors," Journal of Policy Modeling, Elsevier, vol. 37(1), pages 47-64.
    5. Bratu, Mihaela, 2013. "The Assessment And Improvement Of The Accuracy For The Forecast Intervals," Working Papers of Macroeconomic Modelling Seminar 132602, Institute for Economic Forecasting.
    6. Henning Fischer & Ángela Blanco‐FERNÁndez & Peter Winker, 2016. "Predicting Stock Return Volatility: Can We Benefit from Regression Models for Return Intervals?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(2), pages 113-146, March.
    7. Mihaela Simionescu, 2014. "M1 and M2 indicators- new proposed measures for the global accuracy of forecast intervals," Computational Methods in Social Sciences (CMSS), "Nicolae Titulescu" University of Bucharest, Faculty of Economic Sciences, vol. 2(1), pages 54-59, June.

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    More about this item

    Keywords

    Forecast evaluation; interval data; Federal Reserve; monetary policy;
    All these keywords.

    JEL classification:

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

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