Journal of the American Statistical Association · 1983 · 68 citations · 0 references
Social SciencesBiostatisticsDemographyPopulation StudiesStatisticsUncertainty QuantificationConfidence IntervalsEstimation TheoryPopulation ChangePopulation EstimationPopulation ProjectionsStatistical InferenceConfidence Interval EvaluationConfidence Interval MethodologyDemographic ProjectionsPopulation Forecast AccuracyPopulation Forecast ErrorPopulation Projection ModelsProjection IntervalsProjections
Population projections are key elements of many planning and policy studies but are inherently inaccurate. This study of past population projection errors provides a means for constructing confidence intervals for future projections. The authors define an error statistic independent of population size and projection length, then use its stable distribution to build confidence intervals for U.S. population forecasts through 2000. The analysis shows that the error statistic’s component distributions are stable and that simple projection methods outperform more complex ones for total population size.
Abstract Population projections are key elements of many planning and policy studies but are inherently inaccurate. This study of past population projection errors provides a means for constructing confidence intervals for future projections. We first define a statistic to measure projection errors independently of the size of the population and the length of the projection period. A sample of U.S. Census Bureau and United Nations projections indicates that the distributions of components of the error statistic are relatively stable. We then use this information to construct confidence intervals for the total population of the United States through the year 2000. We find that for projections of total population size, simple projection techniques are more accurate than more complex techniques. Key Words: Population forecastsDemographic projectionsPredictionForecastingConfidence intervalsModeling