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.

我调查专业预测员如何响应宏观经济新闻来更新其对产出和通胀的不确定性预测。我从美国（US-SPF）和欧元区（ECB-SPF）的专业预测者调查的密度预测中获得了对单个不确定性的度量，并用它来检验贝叶斯学习的预测，即不确定性应随着预测日期接近目标日期。从经验上讲，我发现该预测有时会被违反，特别是当预报员在最新数据发布中以及随后几个季度中产生狭窄密度预报时遇到意外消息时。有证据表明，响应这些变量的变化，预报员的更新行为也存在很大的异质性。此外，