This paper presents a model that relates properties of the analysts' information environment to the properties of their forecasts. First, we express forecast dispersion and error in the mean forecast in terms of analyst uncertainty and consensus (that is, the degree to which analysts share a common belief). Second, we reverse the relations to show how uncertainty and consensus can be measured by combining forecast dispersion, error in the mean forecast, and the number of forecasts. Third, we show that the quality of common and private information available to analysts can be measured using these same observable variables. The relations we present are intuitive and easily applied in empirical studies.
Barron, Orie E., Oliver Kim, Steve C. Lim, and Douglas E. Stevens. 1998. “Using Analysts' Forecasts to Measure Properties of Analysts' Information Environment”. The Accounting Review 73 (4). American Accounting Association: 421–33.