An Investigation into the Uncertainty Revision Process of Professional Forecasters

Michael Clements, Robert Rich and Joseph Tracy

No 24-19, Working Papers from Federal Reserve Bank of Cleveland

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.

Keywords: Variance forecasts; survey expectations; Bayesian learning; monotonicity tests; inflation forecasts; GDP growth forecasts (search for similar items in EconPapers)
JEL-codes: C53 E17 E37 (search for similar items in EconPapers)
Pages: 31
Date: 2024-09-23
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Journal Article: An Investigation into the Uncertainty Revision Process of Professional Forecasters (2025)
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedcwq:98806

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DOI: 10.26509/frbc-wp-202419

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