This paper examines data on point and probabilistic forecasts of inflation from the Survey of Professional Forecasters. We use this data to evaluate current strategies for the empirical modeling of forecast behavior. In particular, the analysis principally focuses on the relationship between ex post forecast errors and ex ante measures of uncertainty in order to assess the reliability of using proxies based on predictive accuracy to describe changes in predictive confidence. After we adjust the data to account for certain features in the conduct and construct of the survey, we find a significant and robust correlation between observed heteroskedasticity in the consensus forecast errors and forecast uncertainty. We also document that significant compositional effects are present in the data that are economically important in the case of forecast uncertainty, and may be related to differences in respondents' access to information.