Correspondence on the selection of error measures for comparisons among forecasting methods

1995-01-01otherJournal of Forecasting86 citationsclosed

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Forecasting Techniques and Applications Time Series Analysis and Forecasting Statistical and numerical algorithms

Abstract

Abstract Clements and Hendry (1993) proposed the Generalized Forecast Error Second Moment (GFESM) as an improvement to the Mean Square Error in comparing forecasting performance across data series. They based their conclusion on the fact that rankings based on GFESM remain unaltered if the series are linearly transformed. In this paper, we argue that this evaluation ignores other important criteria. Also, their conclusions were illustrated by a simulation study whose relationship to real data was not obvious. Thirdly, prior empirical studies show that the mean square error is an inappropriate measure to serve as a basis for comparison. This undermines the claims made for the GFESM.

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References

1982The accuracy of extrapolation (time series) methods: Results of a forecasting competition

1992Error measures for generalizing about forecasting methods: Empirical comparisons

1993On the limitations of comparing mean square forecast errors

1992The evaluation of extrapolative forecasting methods

1983ARIMA model building and the time series analysis approach to forecasting

1982Note. Evaluation of extrapolative forecasting methods: Results of a survey of academicians and practitioners

1990Evaluating Forecast Performance in an Inventory Control System