Published In

Demografie

Document Type

Article

Publication Date

9-25-2025

Abstract

Population forecasts produced by governments at all levels are used in the public sector, the private sector, and by researchers. They have been primarily produced using deterministic methods. This paper shows how a method for producing measures of uncertainty can be applied to existing subnational population forecasts while meeting several important criteria, including the concept of utility. The paper includes an assessment of the efficacy of the method by: (1) examining the change in uncertainty intervals it produces by population size and population growth rate; and (2) comparing the width and temporal change of the uncertainty intervals it produces to the width and temporal change of uncertainty intervals produced by a Bayesian approach. The approach follows the logic of the Espenshade-Tayman method for producing confidence intervals in conjunction with ARIMA equations to construct a probabilistic interval around the total populations forecasted from the Cohort Component Method, the typical approach used by demographers. The paper finds that population size and population 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. This approach appears to be well-suited for generating probabilistic population forecasts in the United States and elsewhere where these forecasts are routinely produced. It has a higher level of utility, is simpler, and is more accessible to those tasked with producing measures of uncertainty around population forecasts.

Rights

Copyright (c) 2025 The Authors

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

DOI

10.54694/dem.0365

Persistent Identifier

https://archives.pdx.edu/ds/psu/44174

Publisher

Czech Statistical Office

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