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Uncertain population forecasting.

J M Alho, B D Spencer

Journal of the American Statistical Association

|June 1, 1985

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Population forecast errors stem from initial population inaccuracies and vital rate prediction errors. This study develops prediction intervals using the Leslie growth model to quantify these forecast uncertainties.

Area of Science:

Demography
Population Studies
Statistical Modeling

Background:

Population forecasts are crucial for planning but are subject to inherent errors.
Errors originate from initial population data and inaccuracies in predicting future vital rates.
The linear Leslie growth model is a standard tool for population projection.

Purpose of the Study:

To analyze error propagation in population forecasts using the Leslie growth model.
To develop statistically sound prediction intervals for future population sizes.
To compare model-derived intervals with existing forecast uncertainty measures, such as U.S. Census Bureau intervals.

Main Methods:

Utilizing the linear Leslie growth model to simulate error propagation.
Deriving vital rate predictions from a parametric statistical model.

Keywords:

Americas Comparative Studies Data Analysis Demographic Analysis Developed Countries Developing Countries Error Sources Estimation Technics Mathematical Model Measurement Methodological Studies Models, Theoretical North America Northern America Population Forecast Population Projection Reliability Research Methodology United States

Related Experiment Videos

Employing mixed estimation to incorporate expert opinion and robust regression to assess model misspecification.

Main Results:

Quantified the impact of initial population and vital rate errors on forecast accuracy.
Developed prediction intervals that provide a range for future population estimates.
Demonstrated the utility of the developed intervals in assessing forecast reliability.

Conclusions:

Errors in initial population and vital rates significantly influence forecast accuracy.
The developed prediction intervals offer a robust method for quantifying population forecast uncertainty.
The methodology provides a framework for improving the reliability of demographic projections.