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A New Approach to Probabilistic County Population Forecasting with an Example Application to West Texas

Author

Listed:
  • David A. Swanson

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

  • Jeff Tayman

    (Tayman Demographics)

  • Mike Cline

    (North Carolina Office of State Budget and Management)

Abstract

This paper shows how measures of uncertainty can be applied to existing subnational population forecasts using the 107 counties that make up West Texas as a case study. The measures of forecast uncertainty are relatively easy to calculate and meet several important criteria routinely applied by state and local demographers. We also report the results of two independent comparisons supporting the argument that our approach is valid. The paper concludes it is well-suited for developing probabilistic population forecasts in the United States and elsewhere.

Suggested Citation

  • David A. Swanson & Jeff Tayman & Mike Cline, 2025. "A New Approach to Probabilistic County Population Forecasting with an Example Application to West Texas," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 44(4), pages 1-20, August.
    • Handle: RePEc:kap:poprpr:v:44:y:2025:i:4:d:10.1007_s11113-025-09961-3
    DOI: 10.1007/s11113-025-09961-3
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    References listed on IDEAS

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    1. Tom Wilson & Fiona Shalley, 2019. "Subnational population forecasts: Do users want to know about uncertainty?," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(13), pages 367-392.
    2. Jeff Tayman & Stanley Smith & Jeffrey Lin, 2007. "Precision, bias, and uncertainty for state population forecasts: an exploratory analysis of time series models," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 26(3), pages 347-369, June.
    3. Thomas Espenshade & Jeffrey Tayman, 1982. "Confidence intervals for postcensal state population estimates," Demography, Springer;Population Association of America (PAA), vol. 19(2), pages 191-210, May.
    4. Goodwin, Paul, 2015. "When simple alternatives to Bayes formula work well: Reducing the cognitive load when updating probability forecasts," Journal of Business Research, Elsevier, vol. 68(8), pages 1686-1691.
    5. Leontine Alkema & Patrick Gerland & Adrian Raftery & John Wilmoth, 2015. "The United Nations Probabilistic Population Projections: An Introduction to Demographic Forecasting with Uncertainty," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 37, pages 19-24, Spring.
    6. Raftery, Adrian E. & Ševčíková, Hana, 2023. "Probabilistic population forecasting: Short to very long-term," International Journal of Forecasting, Elsevier, vol. 39(1), pages 73-97.
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