Relying on the well-established theoretical result that uncertainty has a common and
an idiosyncratic component, we propose a new measure of earnings forecast uncertainty
as the sum of dispersion among analysts and the variance of mean forecast errors
estimated by a GARCH model. The new measure is based on both common and private
information available to analysts at the time they make their forecasts. Hence, it
alleviates some of the limitations of other commonly used proxies for forecast
uncertainty in the literature. Using analysts’ earnings forecasts, we find direct evidence
of the new measure’s superior performance.