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Measuring temporal stability in regression models of population estimation

M Mandell, J Tayman

    Demography
    |February 1, 1982
    PubMed
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    This study presents a new way to measure the stability of regression models for population estimates. Findings show variable type and measurement are key to accuracy, with coefficient stability having less impact than previously thought.

    Area of Science:

    • Demography
    • Statistical Modeling

    Background:

    • Subnational population estimates rely on regression models.
    • Assessing the temporal stability of these models is crucial for reliable estimates.

    Purpose of the Study:

    • To introduce an empirical indicator for measuring the temporal stability of regression models.
    • To evaluate the impact of temporal stability on subnational population estimate accuracy.

    Main Methods:

    • Analysis of 67 Florida counties using ratio-correlation and difference-correlation models for 1970 population estimates.
    • Comparison of eight regression specifications using a quantitative measure of relative estimate accuracy.

    Main Results:

    • Variable measurement and type significantly influence the accuracy of population estimates.

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  • Temporal stability of regression coefficients impacts estimation errors, but to a lesser extent than indicated in existing literature.
  • Conclusions:

    • The developed indicator provides a novel approach to assessing model temporal stability.
    • Accuracy in subnational population estimation is more dependent on data quality than on coefficient temporal stability alone.