  The interest in the wisdom of crowds stems mainly from the possibility of
combining independent forecasts from experts in the hope that many expert minds
are better than a few. Hence the relevant subject of study nowadays is the Vox
Expertorum rather than Galton's original Vox Populi. Here we use the Federal
Reserve Bank of Philadelphia's Survey of Professional Forecasters to analyze
$15455$ forecasting contests to predict a variety of economic indicators. We
find that the median has advantages over the mean as a method to combine the
experts' estimates: the odds that the crowd beats all participants of a
forecasting contest is $0.015$ when the aggregation is given by the mean and
$0.026$ when it is given by the median. In addition, the median is always
guaranteed to beat the majority of the participants, whereas the mean beats
that majority in 67 percent of the forecasts only. Both aggregation methods
yield a $20$ percent error on the average, which must be contrasted with the
$15$ percent error of the contests' winners. A standard time series forecasting
algorithm, the ARIMA model, yields a $31$ percent error on the average.
However, since the expected error of a randomly selected forecaster is about
$22$ percent, our conclusion is that selective attention is the most likely
explanation for the mysterious high accuracy of the crowd reported in the
literature.
