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An Investigation into the Uncertainty Revision Process of Professional Forecasters

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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 the first 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

  • Michael Clements & Robert W. Rich & Joseph Tracy, 2024. "An Investigation into the Uncertainty Revision Process of Professional Forecasters," Working Papers 24-19, Federal Reserve Bank of Cleveland.
    • Handle: RePEc:fip:fedcwq:98806
    DOI: 10.26509/frbc-wp-202419
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    5. Andrew Patton & Allan Timmermann, 2012. "Forecast Rationality Tests Based on Multi-Horizon Bounds," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 1-17.
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    9. Glas, Alexander, 2020. "Five dimensions of the uncertainty–disagreement linkage," International Journal of Forecasting, Elsevier, vol. 36(2), pages 607-627.
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    12. Michael P. Clements, 2022. "Forecaster Efficiency, Accuracy, and Disagreement: Evidence Using Individual‐Level Survey Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(2-3), pages 537-568, March.
    13. Manzan, Sebastiano, 2021. "Are professional forecasters Bayesian?," Journal of Economic Dynamics and Control, Elsevier, vol. 123(C).
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    17. Dean Croushore & Tom Stark, 2019. "Fifty Years of the Survey of Professional Forecasters," Economic Insights, Federal Reserve Bank of Philadelphia, vol. 4(4), pages 1-11, October.
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    19. Robert W. Rich & Joseph Tracy, 2021. "All Forecasters Are Not the Same: Time-Varying Predictive Ability across Forecast Environments," Working Papers 21-06, Federal Reserve Bank of Cleveland.
    20. Abigail Haddow & Chris Hare & John Hooley & Tamarah Shakir, 2013. "Macroeconomic uncertainty: what is it, how can we measure it and why does it matter?," Bank of England Quarterly Bulletin, Bank of England, vol. 53(2), pages 100-109.
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    22. Dean Croushore, 1993. "Introducing: the survey of professional forecasters," Business Review, Federal Reserve Bank of Philadelphia, issue Nov, pages 3-15.
    23. Robert Rich & Joseph Tracy, 2010. "The Relationships among Expected Inflation, Disagreement, and Uncertainty: Evidence from Matched Point and Density Forecasts," The Review of Economics and Statistics, MIT Press, vol. 92(1), pages 200-207, February.
    24. Krüger, Fabian & Pavlova, Lora, 2024. "Quantifying subjective uncertainty in survey expectations," International Journal of Forecasting, Elsevier, vol. 40(2), pages 796-810.
    25. Patton, Andrew J. & Timmermann, Allan, 2010. "Why do forecasters disagree? Lessons from the term structure of cross-sectional dispersion," Journal of Monetary Economics, Elsevier, vol. 57(7), pages 803-820, October.
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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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