Probabilistic County Population Forecasts: A New Approach with an Example Application to Washington State.

David Swanson, UC Riverside
Jeff Tayman, University of California, San Diego

This paper shows how measures of uncertainty can be applied to existing subnational population forecasts using the counties of Washington State as a case study. The measures of forecast uncertainty are relatively easy to calculate and meet several important criteria routinely applied by state and local demographers. Because the uncertainty measures are applied to county-level population forecasts based on the Cohort Component Method (CCM), they link probabilistic sub-national forecast uncertainty to demographic theory. Analysis of the resulting forecast interval widths show that population size and growth rate are related to the width of the forecast intervals, with size being the stronger predictor, and the intervals from the proposed method are not dissimilar to those produced by a Bayesian approach. The paper includes a discussion of this approach and concludes that it is well-suited for developing probabilistic subnational population forecasts in the United States and elsewhere.

Keywords: Bayesian methods , Population projections, forecasts, and estimations, Data and Methods

See paper.