We consequently recommend incorporating M-estimators into the evaluation toolkit. The empirical evaluation suggests M-estimators do not overstate forecast error as much as either MAPE or SMAPE and are, therefore, more valid measures of accuracy. These alternatives are symmetrical MAPE (SMAPE) and a class of measures known as M-estimators. From a relative standpoint, we examine two alternatives to MAPE, both sharing with it, the important conceptual feature of using most of the information about error. We base this argument on logical grounds and support it empirically, using a sample of population forecasts for counties. Normatively, we argue that MAPE does not meet the criterion of validity because as a summary measure it overstates the error found in a population forecast. While MAPE has many desirable criteria, we argue from both normative and relative standpoints that the widespread practice of exclusively using it for evaluating population forecasts should be changed. The mean absolute percent error (MAPE) is the summary measure most often used for evaluating the accuracy of population forecasts.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |