A Bayesian Approach to Inference on Probabilistic Surveys

Federico Bassetti (), Roberto Casarin and Marco Del Negro

No 1025, Staff Reports from Federal Reserve Bank of New York

Abstract: We propose a nonparametric Bayesian approach for conducting inference on probabilistic surveys. We use this approach to study whether U.S. Survey of Professional Forecasters density projections for output growth and inflation from 1982 to 2022 are consistent with the noisy rational expectations hypothesis. We find that, in contrast to theory, for horizons close to two years there is no relationship whatsoever between subjective uncertainty and forecast accuracy for output growth density projections, both across forecasters and over time, and only a mild relationship for inflation projections. As the horizon shortens, the relationship becomes one-to-one as theory predicts.

Keywords: Bayesian nonparametrics; probabilistic surveys; noisy rational expectations (search for similar items in EconPapers)
JEL-codes: C11 C14 C53 C82 E31 E32 E37 (search for similar items in EconPapers)
Pages: 135
Date: 2022-07-01
New Economics Papers: this item is included in nep-ecm
Note: Revised August 2024.
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Working Paper: A Bayesian Approach for Inference on Probabilistic Surveys (2024)
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