Are Professional Forecasters Bayesian?
I investigate how professional forecasters update their uncertainty forecasts of output and inﬂation in response to macroeconomic news. I obtain a measure of individual uncertainty from the density forecasts of the Survey of Professional Forecasters for the United States (US-SPF) and the Euro area (ECB-SPF) and use it to test the prediction of Bayesian learning that uncertainty should decline as the forecast date nears the target date. Empirically, I ﬁnd that the prediction is occasionally violated, in particular when forecasters experience unexpected news in the most recent data release, and following quarters in which they produce narrow density forecasts.The evidence indicates also signiﬁcant heterogeneity in the updating behavior of forecasters in response to changes in these variables. In addition, I propose a method to solve the problem of the truncation of the density forecasts that occurs when a signiﬁcant amount of probability is assigned to the open intervals.