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Evaluating Population Forecast Accuracy: A Regression Approach Using County Data
Population Research and Policy Review
|April 9, 2011
Summary
This study uses individual county data to analyze population forecast accuracy, finding that population size and growth rate significantly impact predictions. Regression models reveal key determinants for more precise demographic forecasting.
Area of Science:
- Demography
- Population Studies
- Geographic Analysis
Background:
- Previous research on population forecast accuracy primarily used aggregate data.
- Focus on average errors for places with specific size or growth rate characteristics.
Purpose of the Study:
- Investigate population forecast accuracy using regression models on individual place data.
- Examine the influence of population size and growth rate on forecast precision.
- Identify additional explanatory variables affecting forecast accuracy.
Main Methods:
- Utilized decennial census data (1900-2000) for 2,482 US counties.
- Constructed numerous county population forecasts and calculated 10- and 20-year forecast errors.
- Developed and evaluated regression models linking population dynamics to forecast accuracy.
Main Results:
- Confirmed findings from previous aggregate studies.
- Uncovered new insights into factors influencing forecast accuracy at the individual county level.
- Quantified the relative contributions of various explanatory variables.
Conclusions:
- Regression models using individual place data are powerful tools for understanding population forecast accuracy.
- This approach offers a more nuanced investigation into the determinants of forecast errors.
- Highlights under-utilization of individual-level data in demographic forecasting research.
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