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Using Synthetic Adjustments and Controlling to Improve County Population Forecasts from the Hamilton–Perry Method

Author

Listed:
  • Jeff Tayman

    (University of California San Diego)

  • David A. Swanson

    (University of California Riverside
    University of Washington)

  • Jack Baker

    (Transamerica Life, Underwriting Modernization – Research & Development)

Abstract

Tayman and Swanson (J Popul Res 34(3):209–231, 2017) found in Washington State counties that a forecast based on the Hamilton–Perry method using a synthetic adjustment (SYN) of cohort change ratios and child-woman ratios had greater accuracy and less bias compared to forecasts holding these ratios constant (CONST). In this paper, we assess the robustness of SYN’s efficacy by evaluating forecast accuracy, bias, and distributional error across age groups in counties nationwide. We also investigate whether forecast errors and their patterns change for SYN and CONST if forecasts by age and gender are adjusted to an independent total population forecast for each county. Our main findings are as follows: (1) SYN lowers forecast error compared to CONST whether the forecasts are controlled or not; (2) controlling also leads to the improvements in forecast error, often exceeding those in SYN; and (3) using SYN and controlling together has the greatest effect in reducing forecast error. These findings remain after controlling for population size and growth rate, but the positive impacts on forecast error of SYN and controlling are most evident in counties with less than 30,000 population and that grow by 15% or more.

Suggested Citation

  • 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.
    • Handle: RePEc:kap:poprpr:v:40:y:2021:i:6:d:10.1007_s11113-021-09646-7
    DOI: 10.1007/s11113-021-09646-7
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. Lois Fonseca & Jeff Tayman, 1989. "Postcensal estimates of household income distributions," Demography, Springer;Population Association of America (PAA), vol. 26(1), pages 149-159, February.
    5. 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.
    6. Jeff Tayman & Stanley Smith & Stefan Rayer, 2011. "Evaluating Population Forecast Accuracy: A Regression Approach Using County Data," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 30(2), pages 235-262, April.
    7. 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.
    8. 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.
    9. Green, Kesten C. & Armstrong, J. Scott, 2015. "Simple versus complex forecasting: The evidence," Journal of Business Research, Elsevier, vol. 68(8), pages 1678-1685.
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    Cited by:

    1. 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.
    2. 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.
    3. Philip Rees & Tom Wilson, 2023. "Accuracy of Local Authority Population Forecasts Produced by a New Minimal Data Model: A Case Study of England," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 42(6), pages 1-30, December.

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