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Mean absolute percentage error and bias in economic forecasting

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  • McKenzie, Jordi

Abstract

This article develops a simple theoretical framework to show how forecasters may bias downward point predictions under the assumption that the asymmetric loss function is directly related to the (Mean) Absolute Percentage Error (M)APE.

Suggested Citation

  • McKenzie, Jordi, 2011. "Mean absolute percentage error and bias in economic forecasting," Economics Letters, Elsevier, vol. 113(3), pages 259-262.
    • Handle: RePEc:eee:ecolet:v:113:y:2011:i:3:p:259-262
    DOI: 10.1016/j.econlet.2011.08.010
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    References listed on IDEAS

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    1. Engelberg, Joseph & Manski, Charles F. & Williams, Jared, 2009. "Comparing the Point Predictions and Subjective Probability Distributions of Professional Forecasters," Journal of Business & Economic Statistics, American Statistical Association, vol. 27, pages 30-41.
    2. Graham Elliott & Allan Timmermann, 2016. "Economic Forecasting," Economics Books, Princeton University Press, edition 1, number 10740.
    3. Gu, Zhaoyang & Wu, Joanna Shuang, 2003. "Earnings skewness and analyst forecast bias," Journal of Accounting and Economics, Elsevier, vol. 35(1), pages 5-29, April.
    4. Goodwin, Paul & Lawton, Richard, 1999. "On the asymmetry of the symmetric MAPE," International Journal of Forecasting, Elsevier, vol. 15(4), pages 405-408, October.
    5. Terence Lim, 2001. "Rationality and Analysts' Forecast Bias," Journal of Finance, American Finance Association, vol. 56(1), pages 369-385, February.
    6. 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.
    7. Zarnowitz, Victor & Lambros, Louis A, 1987. "Consensus and Uncertainty in Economic Prediction," Journal of Political Economy, University of Chicago Press, vol. 95(3), pages 591-621, June.
    8. Makridakis, Spyros, 1993. "Accuracy measures: theoretical and practical concerns," International Journal of Forecasting, Elsevier, vol. 9(4), pages 527-529, December.
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    More about this item

    Keywords

    Mean absolute percentage error; Asymmetric loss; Forecasting bias;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C70 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - General
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General

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