Applicability of the Revised Mean Absolute Percentage Errors (MAPE) Approach to Some Popular Normal and Non-normal Independent Time Series

Louie Ren and Yong Glasure ()

International Advances in Economic Research, 2009, vol. 15, issue 4, 409-420

Abstract: Commonly used Mean Absolute Percentage Errors (MAPE) and the authors’ revised Mean Absolute Percentage Errors (RMAPE) are applied to measure the forecasting accuracy from different Moving Average Methods for independent time series. Simulation results show that both MAPE and RMAPE can only provide sensitive forecasting accuracy measurements on Moving Average Methods when coefficients of variation (c.v.) are smaller than 0.4 or is much greater than 4.0 for those independent time series. For independent time series with moderate c.v.’s, the complexity from the ratios of MAPE and RMAPE will mislead researchers on distinguishing the forecasting accuracies from different Moving Average Methods. The complexity from the ratios will be released only when the c.v. is very small, or when the c.v. is very large. Therefore, when data are from independent time series, the Mean Absolute Deviation (MAD) reveals valid the forecasting accuracies from various Moving Average Methods, but not from MAPE or RMAPE. Copyright International Atlantic Economic Society 2009

Keywords: Mean Absolute Percentage Errors (MAPE); Revised Mean Absolute Percentage Errors (RMAPE); Forecasting accuracy; Coefficient of variation (c.v.); Mean Absolute Deviation (MAD); C10 Econometrics; Statistics; M21 Managerial Economics; M00 Business Administration (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11294-009-9233-8

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