IDEAS home Printed from https://ideas.repec.org/a/bap/journl/180402.html
   My bibliography  Save this article

A Note on Rescaling the Arithmetic Mean for Right-skewed Positive Distributions

Author

Listed:
  • David A. Swanson

    (Department of Sociology, University of California Riverside, U.S.A.)

  • Jeff Tayman

    (Department of Economics, University of California San Diego, U.S.A.)

  • T.M. Bryan

    (McKibben Demographic Research, U.S.A.)

Abstract

When the arithmetic mean (mean) is used as a measure of location for a set of rightskewed positive observations, it is subject to being pulled upward. This upward movement tends to move the mean away from the bulk of the observations, making it less representative of them. One way to deal with this loss of representativeness is to transform the data. A Box-Cox power transformation can make a right-skewed distribution more symmetrical and then a measure of location for the original observations is found by applying an inverse transformation to the center of the transformed data. This approach was used in a series of papers dealing with the Mean Absolute Percent Error (MAPE) as a measure of forecast and estimation error. In this paper, we show that the Box-Cox power transformation can be used more generally with any mean computed for a set of right-skewed positive observations to develop R-MEAN (Rescaled-Mean). We provide a set of examples to illustrate this approach and show its use in an actual application.

Suggested Citation

  • David A. Swanson & Jeff Tayman & T.M. Bryan, 2018. "A Note on Rescaling the Arithmetic Mean for Right-skewed Positive Distributions," Review of Economics & Finance, Better Advances Press, Canada, vol. 14, pages 17-24, November.
  • Handle: RePEc:bap:journl:180402
    as

    Download full text from publisher

    File URL: http://www.bapress.ca/ref/ref-article/1923-7529-2018-04-17-08.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. David Swanson & Jeff Tayman & Charles Barr, 2000. "A note on the measurement of accuracy for subnational demographic estimates," Demography, Springer;Population Association of America (PAA), vol. 37(2), pages 193-201, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jack Baker & David Swanson & Jeff Tayman, 2021. "The Accuracy of Hamilton–Perry Population Projections for Census Tracts in the United States," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 40(6), pages 1341-1354, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Herman O. Stekler, 2008. "Evaluating Consensus Forecasts," Working Papers 2008-007, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    2. Meng Xu & Helge Brunborg & Joel E. Cohen, 2017. "Evaluating multi-regional population projections with Taylor’s law of mean–variance scaling and its generalisation," Journal of Population Research, Springer, vol. 34(1), pages 79-99, March.
    3. Kim, Sungil & Kim, Heeyoung, 2016. "A new metric of absolute percentage error for intermittent demand forecasts," International Journal of Forecasting, Elsevier, vol. 32(3), pages 669-679.
    4. David A. Swanson, 2015. "On the Relationship among Values of the Same Summary Measure of Error when it is used across Multiple Characteristics at the Same Point in Time: An Examination of MALPE and MAPE," Review of Economics & Finance, Better Advances Press, Canada, vol. 5, pages 1-14, August.
    5. George Hough & David Swanson, 2006. "An evaluation of the American Community Survey: results from the Oregon test site," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 25(3), pages 257-273, June.
    6. 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.
    7. Ji Wu & Xian Cheng & Stephen Shaoyi Liao, 2020. "Tourism forecast combination using the stochastic frontier analysis technique," Tourism Economics, , vol. 26(7), pages 1086-1107, November.
    8. Spiliotis, Evangelos & Nikolopoulos, Konstantinos & Assimakopoulos, Vassilios, 2019. "Tales from tails: On the empirical distributions of forecasting errors and their implication to risk," International Journal of Forecasting, Elsevier, vol. 35(2), pages 687-698.
    9. Louie Ren & Yong Glasure, 2009. "Applicability of the Revised Mean Absolute Percentage Errors (MAPE) Approach to Some Popular Normal and Non-normal Independent Time Series," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 15(4), pages 409-420, November.
    10. David Swanson & George Hough, 2012. "An Evaluation of Persons per Household (PPH) Estimates Generated by the American Community Survey: A Demographic Perspective," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 31(2), pages 235-266, April.
    11. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    12. Takahiro Yoshida & Daisuke Murakami & Hajime Seya, 2024. "Spatial Prediction of Apartment Rent using Regression-Based and Machine Learning-Based Approaches with a Large Dataset," The Journal of Real Estate Finance and Economics, Springer, vol. 69(1), pages 1-28, July.
    13. Jack Baker & David Swanson & Jeff Tayman, 2023. "Boosted Regression Trees for Small-Area Population Forecasting," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 42(4), pages 1-24, August.

    More about this item

    Keywords

    Asymmetric distribution; Box-Cox Power Transformation; Outlier; R-MEAN;
    All these keywords.

    JEL classification:

    • B41 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Economic Methodology
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bap:journl:180402. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Carlson (email available below). General contact details of provider: http://www.bapress.ca .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.