A couple of recent papers have shifted the focus on disagreement of professional forecasters. When dealing with survey data that includes only fixed event forecasts, e.g. expectation of average annual growth rates, measures of disagreement across forecasters naturally are distorted by a component that mainly reflects the time varying forecast horizon. We use data from the Survey of Professional Forecasters, which reports both fixed event and fixed horizon forecasts, to evaluate different methods for extracting the "fundamental" component of disagreement. Based on the paper's results we suggest two methods to estimate dispersion measures from panels of fixed event forecasts: a moving average transformation of the underlying forecasts and estimation with constant forecast-horizon-effects. Both models are easy to handle and deliver equally well performing results, which show a surprisingly high correlation with the dispersion measures derived from the common fixed horizon forecasts.