An Investigation into the Uncertainty Revision Process of Professional Forecasters

RePEc Short ID: pcl24
ORCID iD: 0000-0001-6329-1341

Clements, Michael; Rich, Robert W.; Tracy, Joseph

doi:10.26509/frbc-wp-202419

Working Paper

An Investigation into the Uncertainty Revision Process of Professional Forecasters

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;

JEL Classification: C53; E17; E37;

https://doi.org/10.26509/frbc-wp-202419