IDEAS home Printed from https://ideas.repec.org/a/kap/poprpr/v44y2025i3d10.1007_s11113-025-09951-5.html
   My bibliography  Save this article

A Simplified Version of the Hamilton–Perry Method for Forecasting Population by Age Group and Gender

Author

Listed:
  • Jeff Tayman

    (Tayman Demographics)

  • David A. Swanson

    (Portland State University
    University of Washington
    University of California Riverside)

Abstract

Following the concept of utility, this paper examines a way to reduce further the time and cost associated with an already cost-effective approach to population forecasting, the Hamilton–Perry method (H–P method), which not only has been found to produce reasonably accurate forecasts but has a wide range of applications, including stable population theory and historical demography. The usual application of the H–P method, H–P-Usual, is based on gender-specific CCRs and CWRs, using the female population in the childbearing years to compute the CWRs. H–P-Usual has potential drawbacks. If population forecasts by age are only needed for the total population, additional time and resources are required to assemble and evaluate the gender-specific CCRs and CWRs. More pressing, especially for subcounty areas, is the issue of small population sizes in the age and gender-specific population cells. One potential solution to these issues is reducing the number of input cells required by the H–P model. We propose a simpler H–P method, H–P-Light, that uses non-gender-specific CCRs and CWRs. We analyzed 10-year forecast errors for US counties and census tracts and found no degradation of performance using H–P-Light in both counties and census tracts. We conclude that H–P-Light is a viable alternative to H–P-Usual for producing age-specific population forecasts for the total population and, if needed, by gender.

Suggested Citation

  • Jeff Tayman & David A. Swanson, 2025. "A Simplified Version of the Hamilton–Perry Method for Forecasting Population by Age Group and Gender," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 44(3), pages 1-33, June.
  • Handle: RePEc:kap:poprpr:v:44:y:2025:i:3:d:10.1007_s11113-025-09951-5
    DOI: 10.1007/s11113-025-09951-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11113-025-09951-5
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s11113-025-09951-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Jeff Tayman & David A. Swanson & Jack Baker, 2021. "Using Synthetic Adjustments and Controlling to Improve County Population Forecasts from the Hamilton–Perry Method," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 40(6), pages 1355-1383, December.
    2. 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.
    3. Jeff Tayman & David Swanson, 1996. "On the utility of population forecasts," Demography, Springer;Population Association of America (PAA), vol. 33(4), pages 523-528, November.
    4. Neal Marquez & Xiaoqi Bao & Eileen Kazura & Jessica Lapham & Priya Sarma & Crystal Yu & Christine Leibbrand & Sara Curran, 2024. "An Evaluation of Projection Methods for Detailed Small Area Projections: An Application and Validation to King County, Washington," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 43(2), pages 1-29, April.
    5. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    6. Jeff Tayman & David A. Swanson, 2017. "Using modified cohort change and child-woman ratios in the Hamilton–Perry forecasting method," Journal of Population Research, Springer, vol. 34(3), pages 209-231, September.
    7. Armstrong, J. Scott, 2006. "Findings from evidence-based forecasting: Methods for reducing forecast error," International Journal of Forecasting, Elsevier, vol. 22(3), pages 583-598.
    Full references (including those not matched with items on IDEAS)

    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. Tom Wilson & Irina Grossman & Monica Alexander & Phil Rees & Jeromey Temple, 2022. "Methods for Small Area Population Forecasts: State-of-the-Art and Research Needs," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(3), pages 865-898, June.
    2. Takashi Inoue & Nozomu Inoue, 2024. "The Future Process of Japan’s Population Aging: A Cluster Analysis Using Small Area Population Projection Data," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 43(4), pages 1-26, August.
    3. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    4. Lee, Yun Shin & Scholtes, Stefan, 2014. "Empirical prediction intervals revisited," International Journal of Forecasting, Elsevier, vol. 30(2), pages 217-234.
    5. Michael P. Cameron & William Cochrane, 2015. "Using Land-Use Modelling to Statistically Downscale Population Projections to Small Areas," Working Papers in Economics 15/12, University of Waikato.
    6. Wang, Shengjie & Kang, Yanfei & Petropoulos, Fotios, 2024. "Combining probabilistic forecasts of intermittent demand," European Journal of Operational Research, Elsevier, vol. 315(3), pages 1038-1048.
    7. Armstrong, J. Scott, 2007. "Significance tests harm progress in forecasting," International Journal of Forecasting, Elsevier, vol. 23(2), pages 321-327.
    8. Osman Gulseven, 2016. "Forecasting Population and Demographic Composition of Kuwait Until 2030," International Journal of Economics and Financial Issues, Econjournals, vol. 6(4), pages 1429-1435.
    9. Robert Fildes & Paul Goodwin, 2007. "Against Your Better Judgment? How Organizations Can Improve Their Use of Management Judgment in Forecasting," Interfaces, INFORMS, vol. 37(6), pages 570-576, December.
    10. Makridakis, Spyros & Hyndman, Rob J. & Petropoulos, Fotios, 2020. "Forecasting in social settings: The state of the art," International Journal of Forecasting, Elsevier, vol. 36(1), pages 15-28.
    11. Arvan, Meysam & Fahimnia, Behnam & Reisi, Mohsen & Siemsen, Enno, 2019. "Integrating human judgement into quantitative forecasting methods: A review," Omega, Elsevier, vol. 86(C), pages 237-252.
    12. V. Candila & O. Cepni & G. M. Gallo & R. Gupta, 2024. "Influence of Local and Global Economic Policy Uncertainty on the volatility of US state-level equity returns: Evidence from a GARCH-MIDAS approach with Shrinkage and Cluster Analysis," Working Paper CRENoS 202414, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    13. Zeng, Michael A., 2018. "Foresight by online communities – The case of renewable energies," Technological Forecasting and Social Change, Elsevier, vol. 129(C), pages 27-42.
    14. Tom Wilson, 2022. "Preparing local area population forecasts using a bi-regional cohort-component model without the need for local migration data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 46(32), pages 919-956.
    15. Alvarado-Valencia, Jorge & Barrero, Lope H. & Önkal, Dilek & Dennerlein, Jack T., 2017. "Expertise, credibility of system forecasts and integration methods in judgmental demand forecasting," International Journal of Forecasting, Elsevier, vol. 33(1), pages 298-313.
    16. 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.
    17. Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos, 2011. "Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 635-660, July.
    18. Jeff Tayman, 2011. "Assessing Uncertainty in Small Area Forecasts: State of the Practice and Implementation Strategy," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 30(5), pages 781-800, October.
    19. Armstrong, J. Scott & Fildes, Robert, 2006. "Making progress in forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 433-441.
    20. Jeff Tayman & David A. Swanson & Jack Baker, 2021. "Using Synthetic Adjustments and Controlling to Improve County Population Forecasts from the Hamilton–Perry Method," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 40(6), pages 1355-1383, December.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    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:kap:poprpr:v:44:y:2025:i:3:d:10.1007_s11113-025-09951-5. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.