A Bayesian Approach to Inference on Probabilistic Surveys

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 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.

Keywords: Bayesian interface; Bayesian nonparametric; Survey of Professional Forecasters; noisy rational expectations;

JEL Classification: C11; C13; C15; C32; C58; G12;

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Bibliographic Information

Provider: Federal Reserve Bank of New York

Part of Series: Staff Reports

Publication Date: 2022-07-01

Number: 1025