1. An Investigation into the Uncertainty Revision Process of Professional Forecasters
- Author
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Clements, Michael P., Rich, Robert, and Tracy, Joseph
- Subjects
Market trend/market analysis ,Bayesian statistical decision theory -- Usage ,Analysis of variance - 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 study 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, August 13, 2024 I. Introduction The study of expectations and the process underlying their formation remains a topic of considerable interest and importance. The recently published Handbook of Economic Expectations [...]
- Published
- 2024