A Bayesian Approach for Inference on Probabilistic Surveys

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Staff Reports

A Bayesian Approach for Inference on Probabilistic Surveys

Number 1025
July 2022

JEL classification: C11, C13, C15, C32, C58, G12

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

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 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 the theory would predict.

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Author Disclosure Statement(s)

Marco Del Negro
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.

Federico Bassetti
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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