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Population forecast accuracy: does the choice of summary measure of error matter?


  • Stefan Rayer



Population projections are judged primarily by their accuracy. The most commonly used measure for the precision component of accuracy is the mean absolute percent error (MAPE). Recently, the MAPE has been criticized for overstating forecast error and other error measures have been proposed. This study compares the MAPE with two alternative measures of forecast error, the Median APE and an M-estimator. In addition, the paper also investigates forecast bias. The analysis extends previous studies of forecast error by examining a wide range of trend extrapolation techniques using a dataset that spans a century for a large sample of counties in the US. The main objective is to determine whether the choice of summary measure of error makes a difference from a practitioner’s standpoint. The paper finds that the MAPE indeed produces error values that exceed the robust measures. However, except for situations where extreme outliers rendered the MAPE meaningless, and which are rare in real world applications, there was not a single instance where using an alternative summary measure of error would have led to a fundamentally different evaluation of the projections. Moreover, where differences existed, it was not always clear that the values and patterns provided by the robust measures were necessarily more correct than those obtained with the MAPE. While research into refinements and alternatives to the MAPE and mean algebraic percent error are worthwhile, consideration of additional evaluation procedures that go beyond a single criterion might provide more benefits to producers and users of population forecasts. Copyright Springer Science+Business Media B.V. 2007

Suggested Citation

  • Stefan Rayer, 2007. "Population forecast accuracy: does the choice of summary measure of error matter?," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 26(2), pages 163-184, April.
  • Handle: RePEc:kap:poprpr:v:26:y:2007:i:2:p:163-184
    DOI: 10.1007/s11113-007-9030-0

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    References listed on IDEAS

    1. Yokuma, J. Thomas & Armstrong, J. Scott, 1995. "Beyond accuracy: Comparison of criteria used to select forecasting methods," International Journal of Forecasting, Elsevier, vol. 11(4), pages 591-597, December.
    2. Ahlburg, Dennis A., 1992. "Error measures and the choice of a forecast method," International Journal of Forecasting, Elsevier, vol. 8(1), pages 99-100, June.
    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. Steve Murdock & F. Leistritz & Rita Hamm & Sean-Shong Hwang & Banoo Parpia, 1984. "An assessment of the accuracy of a regional economic-demographic projection model," Demography, Springer;Population Association of America (PAA), vol. 21(3), pages 383-404, August.
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    Cited by:

    1. Stefan Rayer & Stanley Smith & Jeff Tayman, 2009. "Empirical Prediction Intervals for County Population Forecasts," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 28(6), pages 773-793, December.
    2. Seyma Caliskan Cavdar & Alev Dilek Aydin, 2015. "An Empirical Analysis for the Prediction of a Financial Crisis in Turkey through the Use of Forecast Error Measures," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 8(3), pages 1-18, August.
    3. Ma, Lu & Srinivasan, Sivaramakrishnan, 2016. "An empirical assessment of factors affecting the accuracy of target-year synthetic populations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 85(C), pages 247-264.
    4. Stefan Rayer & Stanley Smith, 2014. "Population Projections by Age for Florida and its Counties: Assessing Accuracy and the Impact of Adjustments," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 33(5), pages 747-770, October.


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