A Bayesian Approach for Inference on Probabilistic Surveys

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A Bayesian Approach for Inference on Probabilistic Surveys

Number 1025
July 2022 Revised August 2024

JEL classification: C11, C14, C53, C82, E31, E32

Authors: Federico Bassetti, Roberto Casarin, and Marco Del Negro

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.

Available only in PDF

Author Disclosure Statement(s)

Federico Bassetti
The author declares that he has no relevant or material financial interests that relate to the research described in this paper.

Roberto Casarin
The author declares that he has no relevant or material financial interests that relate to the research described in this paper.

Marco Del Negro
The author declares that he has no relevant or material financial interests that relate to the research described in this paper.

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