MAPE-R: A Rescaled Measure of Accuracy for Cross-Sectional Forecasts

David A. Swanson, University of California, Riverside
Jeff Tayman, San Diego Association of Governments (SANDAG)
T. Bryan, T Bryan and Associates

Accurately measuring a population and its attributes at future points in time has been of great interest to demographers. The measures used to evaluate the accuracy of forecasts also have received attention and while accuracy is not the only criteria advocated for evaluating demographic forecasts, it is generally acknowledged to be the most important. We continue the discussion of measures of forecast accuracy by concentrating on a rescaled version of a measure that is arguably the one used most often, Mean Absolute Percent Error (MAPE). The rescaled version, MAPE-R, has not had the benefit of a major empirical test, which is the central focus of this paper. We do this by comparing 10-year population forecasts for U.S. counties to 2000 census counts. We find that the MAPE-R offers a significantly more meaningful representation of average error than MAPE in the presence of outlying errors and provide guidelines for its implementation.

  See paper

Presented in Poster Session 3