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Boosted Regression Trees for Small-Area Population Forecasting

Author

Listed:
  • Jack Baker

    (Farmers Life)

  • David Swanson

    (University of California Riverside
    University of Washington)

  • Jeff Tayman

    (University of California San Diego)

Abstract

Small-area population forecasting, such as the forecasting of age/gender groupings at the level of US Census Tracts, is challenged by thorny issues including (1) small population sizes, (2) frequent and sometimes directionally opposing shifts in population dynamics between censuses, (3) data availability, and (4) the ongoing evolution of the US census geographies. It is, therefore, not surprising that evaluation studies suggest wide-ranging forecast errors. Estimates vary between lows between 10% and 20% and highs sometimes exceeding 100% within any given age/gender interval. Despite its successes, only recently have population forecasters begun to explore the possibilities presented by machine learning. Using 1990 and 2000 census data, we develop 10-year age/gender-structured 2010 population forecasts for 50,965 census tracts in the U.S. using a well-known machine learning technique: boosted regression trees. Using standard ex post facto measures of forecast error (MAPE, MALPE, and MAPE-R), we demonstrate that forecasts based on “out-of-the-box” boosted regression trees have greater accuracy and produce fewer and less extreme outliers than comparison forecasts produced by the Hamilton-Perry method (reported in Baker et al. in Population Res Policy Rev 40:1341–1354, 2021. https://doi.org/10.1007/s11113-020-09601-y ).

Suggested Citation

  • 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.
  • Handle: RePEc:kap:poprpr:v:42:y:2023:i:4:d:10.1007_s11113-023-09795-x
    DOI: 10.1007/s11113-023-09795-x
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    References listed on IDEAS

    as
    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. Tiago M. Fragoso & Wesley Bertoli & Francisco Louzada, 2018. "Bayesian Model Averaging: A Systematic Review and Conceptual Classification," International Statistical Review, International Statistical Institute, vol. 86(1), pages 1-28, April.
    3. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    4. Philip Rees & Paul Norman & Dominic Brown, 2004. "A framework for progressively improving small area population estimates," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(1), pages 5-36, February.
    5. 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.
    6. Stefan Rayer & Stanley Smith, 2014. "Population Projections by Age for Florida and its Counties: Assessing Accuracy and the Impact of Adjustments," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 33(5), pages 747-770, October.
    7. 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.
    8. 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.
    9. 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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