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Further, when respondents are asked point forecast questions, some elect to provide a range, in order to express a degree of uncertainty in their forecast. We find that the use of ranges in point forecast questions, as well as the widths of reported ranges, are positively correlated with the number of bins to which respondents assign positive probability in density forecast questions, and with measures of uncertainty based on the fitted density estimates. Respondents also rate the density forecast questions only slightly harder to answer than the corresponding point forecast ones. Finally, respondents’ estimated forecast uncertainty is meaningfully associated with month-to-month revisions in inflation expectations: greater uncertainty is significantly correlated with larger revisions – qualitatively consistent with Bayesian updating. What is the best way to aggregate the information we elicit regarding probabilistic beliefs and density forecasts? For binary outcomes, such as the perceived percent chance of losing one’s job over the next three months, we report the mean probability across respondents. For density forecasts, we can compute several aggregate measures. Slide 4 shows a stylized depiction of how we approach this. As our aggregate measure of expected inflation, we report the median of individual density means. We pick the density mean, which represents an individual’s expectation for the rate of inflation, as our measure of central tendency, and use the median to aggregate across respondents, since the median is less sensitive to outliers.