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An Investigation into the Uncertainty Revision Process of Professional Forecasters

Author

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  • Clements, Michael P.
  • Rich, Robert W.
  • Tracy, Joseph

Abstract

Following Manzan (2021), this paper examines how professional forecasters revise their fixed-event uncertainty (variance) forecasts and tests the Bayesian learning prediction that variance forecasts should decrease as the horizon shortens. We show that Manzan's (2021) use of first moment “efficiency” tests are not applicable to studying revisions of variance forecasts. Instead, we employ monotonicity tests developed by Patton and Timmermann (2012) in our first known application of these tests to second moments of survey expectations. We find strong evidence that the variance forecasts are consistent with the Bayesian learning prediction of declining monotonicity.

Suggested Citation

  • Clements, Michael P. & Rich, Robert W. & Tracy, Joseph, 2025. "An Investigation into the Uncertainty Revision Process of Professional Forecasters," Journal of Economic Dynamics and Control, Elsevier, vol. 173(C).
    • Handle: RePEc:eee:dyncon:v:173:y:2025:i:c:s0165188925000260
    DOI: 10.1016/j.jedc.2025.105060
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    More about this item

    Keywords

    Variance forecasts; Survey expectations; Bayesian learning; Monotonicity tests; Inflation forecasts; GDP growth forecasts;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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