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Using modified cohort change and child-woman ratios in the Hamilton–Perry forecasting method

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  • Jeff Tayman

    (University of California, San Diego)

  • David A. Swanson

    (University of California, Riverside)

Abstract

The Hamilton–Perry method, which uses cohort change ratios (CCR) and child-woman ratios (CWR), has gained acceptance as research has demonstrated its practical value and accuracy in forecasting population composition. Assessments of this method have been based on the usual assumption that CCRs and CWRs developed over the base period are held constant over the forecast horizon. We propose several approaches for modifying CCRs and CWRs over the forecast horizon. These alternatives are averaging and trending these ratios and a synthetic method that bases local CCRs and CWRs changes on state-level changes in CCRs and CWRs. We evaluate the errors for these alternatives against the errors holding the CCRs and CWRs constant for counties in Washington State and for census tracts in New Mexico. The evaluation suggests that averaging or trending CCRs and CWRs are not worthwhile strategies, but the synthetic method reduces errors compared to holding the ratios constant over the horizon.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joprea:v:34:y:2017:i:3:d:10.1007_s12546-017-9190-7
    DOI: 10.1007/s12546-017-9190-7
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Tom Wilson, 2016. "Evaluation of Alternative Cohort-Component Models for Local Area Population Forecasts," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 35(2), pages 241-261, April.
    4. David Swanson & Alan Schlottmann & Bob Schmidt, 2010. "Forecasting the Population of Census Tracts by Age and Sex: An Example of the Hamilton–Perry Method in Action," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 29(1), pages 47-63, February.
    5. Stanley Smith & Jeff Tayman, 2003. "An evaluation of population projections by age," Demography, Springer;Population Association of America (PAA), vol. 40(4), pages 741-757, November.
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    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.
    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. 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.
    4. 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.

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