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Correspondence On the Selection of Error Measures for Comparisons Among Forecasting Methods

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  • JS Armstrong

    (The Wharton School - University of Pennsylvania)

  • Robert Fildes

    (The Management School - Lancaster University - UK)

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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Bibliographic Info

Paper provided by EconWPA in its series General Economics and Teaching with number 0412002.

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Length: 4 pages
Date of creation: 06 Dec 2004
Date of revision:
Handle: RePEc:wpa:wuwpgt:0412002

Note: Type of Document - pdf; pages: 4
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Web page: http://128.118.178.162

Related research

Keywords: Accuracy Forecast evaluation Loss functions;

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References

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  1. Zellner, Arnold, 1986. "A tale of forecasting 1001 series : The Bayesian knight strikes again," International Journal of Forecasting, Elsevier, vol. 2(4), pages 491-494.
  2. Makridakis, Spyros & Hibon, Michele & Lusk, Ed & Belhadjali, Moncef, 1987. "Confidence intervals: An empirical investigation of the series in the M-competition," International Journal of Forecasting, Elsevier, vol. 3(3-4), pages 489-508.
  3. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
  4. Clements, M.P. & Hendry, D., 1992. "On the Limitations of Comparing Mean Square Forecast Errors," Economics Series Working Papers 99138, University of Oxford, Department of Economics.
  5. Thompson, Patrick A., 1990. "An MSE statistic for comparing forecast accuracy across series," International Journal of Forecasting, Elsevier, vol. 6(2), pages 219-227, July.
  6. Robert Carbone & JS Armstrong, 2004. "Evaluation of Extrapolative Forecasting Methods: Results of a Survey of Academicians and Practitioners," General Economics and Teaching 0412008, EconWPA.
  7. Murphy, Allan H. & Winkler, Robert L., 1992. "Diagnostic verification of probability forecasts," International Journal of Forecasting, Elsevier, vol. 7(4), pages 435-455, March.
  8. Fildes, Robert, 1992. "The evaluation of extrapolative forecasting methods," International Journal of Forecasting, Elsevier, vol. 8(1), pages 81-98, June.
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