Using Synthetic Adjustments and Controlling to Improve County Population Forecasts from the Hamilton–Perry Method
Bibliographic Data
| ID | 11291666 |
|---|---|
| Authors | Jeff Tayman (0000-0003-3572-209X, University of California San Diego, corresponding author), David A Swanson (0000-0003-4284-9478, University of Washington), Jack Baker (0009-0007-5797-4390), Jack W Baker (0000-0003-2744-9599) |
| Year | 2021 |
| Volume | 40 |
| Issue | 6 |
| Pages | 1355-1383 |
| Publication date | 2021-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Population Research and Policy Review (JOURNAL) |
| Journal identifiers | ISSN: 0167-5923 • E-ISSN: 1573-7829 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s11113-021-09646-7 |
| OpenAlex | W3136365655 |
| Language | EN |
| Citations received | 4 |
| References cited | 22 |
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
Biology · Econometrics · Economics · Forecast error · Observational error · Population · Robustness (evolution · Statistics · demographic modeling and climate adaptation · Demography · Economics of Agriculture and Food Markets · Insurance, Mortality, Demography, Risk Management · Mathematics
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| Unique citing works | 4 |
|---|---|
| Citations per year | 1 |
| Citation span | 2022 - 2025 (4) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 4 |