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

Jeff Tayman (), David A. Swanson and Jack Baker
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Jeff Tayman: University of California San Diego
David A. Swanson: University of California Riverside
Jack Baker: Transamerica Life, Underwriting Modernization – Research & Development

Population Research and Policy Review, 2021, vol. 40, issue 6, No 9, 1355-1383

Abstract: 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.

Keywords: Hamilton–Perry method; Synthetic adjustment; Forecast evaluation (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11113-021-09646-7

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