MAPE-R: a rescaled measure of accuracy for cross-sectional subnational population forecasts
By David A. Swanson, J. Tayman, T. Bryan. 2011
137 citations recorded by the source corpus.
Accurately measuring a population and its attributes at past, present, and future points in time has been of great interest to demographers. Within discussions of forecast accuracy, demographers have often been criticized for their inaccurate prognostications of the future. Discussions of methods and data are usually at the centre of these criticisms, along with suggestions for providing an idea of forecast uncertainty. The measures used to evaluate the accuracy of forecasts also have received attention and while accuracy is not the only criterion advocated for evaluating demographic forecasts, it is generally acknowledged to be the most important. In this paper, 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 in evaluating cross-sectional, subnational forecasts, 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 substantial outlying errors, and we provide guidelines for its implementation.
Subfields: Demography, General Health Professions, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/dd1fe9a7976db62bd85689fd167a002bd87a9083
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In Management Science and Operations Research
Citations in the corpus
- Gaussian Process Regression for In Situ Capacity Estimation of Lithium-Ion Batteries (2017, unknown): “Because percentage errors are normalized, they can be used to compare forecast performance across datasets with different absolute cell capacities, as is the case in this study [29].”
- Allometric equations for estimating tree aboveground biomass in evergreen broadleaf forests of Viet Nam (2016, methodology): “Models were compared in terms of percent bias, root mean square percentage error (RMSPE), and mean absolute percent error (MAPE) (Swanson et al., 2011).”