Population forecast accuracy: does the choice of summary measure of error matter?

Population projections are judged primarily by their accuracy. The most commonly used measure for the precision component of accuracy is the mean absolute percent error (MAPE). Recently, the MAPE has been criticized for overstating forecast error and other error measures have been proposed. This study compares the MAPE with two alternative measures of forecast error, the Median APE and an M-estimator. In addition, the paper also investigates forecast bias. The analysis extends previous studies of forecast error by examining a wide range of trend extrapolation techniques using a dataset that spans a century for a large sample of counties in the US. The main objective is to determine whether the choice of summary measure of error makes a difference from a practitioner’s standpoint. The paper finds that the MAPE indeed produces error values that exceed the robust measures. However, except for situations where extreme outliers rendered the MAPE meaningless, and which are rare in real world applications, there was not a single instance where using an alternative summary measure of error would have led to a fundamentally different evaluation of the projections. Moreover, where differences existed, it was not always clear that the values and patterns provided by the robust measures were necessarily more correct than those obtained with the MAPE. While research into refinements and alternatives to the MAPE and mean algebraic percent error are worthwhile, consideration of additional evaluation procedures that go beyond a single criterion might provide more benefits to producers and users of population forecasts.