MAPE-R: a rescaled measure of accuracy for cross-sectional subnational population forecasts

Abstract

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.

Allometric Equations for Estimating Tree Aboveground Biomass in Tropical Dipterocarp Forests of Vietnam

“…Performance of selected equations were validated and compared to each other and to equations developed for the dipterocarp and pantropic forests in terms of percent bias, root mean square percentage error (RMSPE), and mean absolute percent error (MAPE) [29]. These indicators were calculated for the validation dataset and smaller values were preferred.…”

Accuracy Assessment on Prediction Models for Fetal Weight Based on Maternal Fundal Height

“…The accuracy of prediction is fundamental to ensure that the forecast correctly reflects the future [26][27][28]. In this paper, we have used the following error measurements to assess the efficacy of the different models; mean error (ME), median error (MEDE), mean percentage error (MPE), median percentage error (MEDPE), mean absolute error (MAE), median absolute error (MEDAE), mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), median absolute percentage error (MEDAPE), mean square percentage error (MSPE), and root mean square percentage error (RMSPE) [25,26].…”

“…On the other hand, t assessed by MAE, MEDA can be explained by MAP is commonly used in cros over a period of forecasts MEDAPE [26].…”

Vegetation Community, Foliar Nitrogen, and Temperature Effects on Tundra CO2Exchange across a Soil Moisture Gradient

“…Non-scale-dependent error estimates adjust for the population size by using a percentage error. Since percentage errors are not scale-dependent, they can be used to compare performance across data sets (Swanson et al, 2011). As in Chen et al (2009), two relative statistics, the Relative Mean Absolute Error (RMAE) and the Median Absolute Percentage Error (MedAPE), were selected as the non-scale-dependent estimates of error.…”

“…RMAE is computed as the Mean Absolute Error (MAE) divided by the corresponding average value of the dependent variable and is more sensitive to errors if the dependent variable has a large value. In contrast, the value of MedAPE generally gives more weight to percentage errors corresponding to low values for the dependent variable (Chen et al, 2009;Swanson et al, 2011). The coefficient of determination (r 2 ) is the most common statistic given describing the fit of an empirical model and can be interpreted as the proportion of the total variation in the dependent variable explained by the independent variable.…”