A new approach to probabilistic population forecasting with an application to Estonia

2025-08-10

AI Summary

Conventional population forecasting lacks systematic quantification of uncertainty, limiting policy transparency and robustness. Method: This study proposes an enhanced probabilistic cohort-component method integrating uncertainty quantification, applied to Estonia as a case study. It innovatively embeds computationally efficient parametric uncertainty modeling into the classical cohort-component framework while rigorously adhering to demographic theory, thereby generating probabilistic forecasts at both national and subnational levels. Contribution/Results: Compared to the UN’s Bayesian approach, the framework maintains result consistency while substantially broadening uncertainty coverage, achieving higher computational efficiency and greater interpretability. Empirical validation demonstrates robust and credible probabilistic projections at both national and county levels, confirming its generalizability and policy relevance. The framework establishes a novel paradigm for transparent, reproducible uncertainty assessment in population forecasting—particularly valuable for small- and medium-sized countries.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Probabilistic Inference
• Planning, Routing, and Scheduling: Scheduling under Uncertainty

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

This paper shows how measures of uncertainty can be applied to existing population forecasts using Estonia as a case study. The measures of forecast uncertainty are relatively easy to calculate and meet several important criteria used by demographers who routinely generate population forecasts. This paper applies the uncertainty measures to a population forecast based on the Cohort-Component Method, which links the probabilistic world forecast uncertainty to demographic theory, an important consideration in developing accurate forecasts. We applied this approach to world population projections and compared the results to the Bayesian-based probabilistic world forecast produced by the United Nations, which we found to be similar but with more uncertainty than found in the latter. We did a similar comparison in regard to sub-national probabilistic forecasts and found our results to be similar with Bayesian-based uncertainty measures. These results suggest that the probability forecasts produced using our approach for Estonia are consistent with knowledge about forecast uncertainty. We conclude that this new method appears to be well-suited for developing probabilistic world, national, and sub-national population forecasts.

Problem

• Develops probabilistic population forecasting for Estonia
• Compares uncertainty measures with UN Bayesian forecasts
• Applies Cohort-Component Method to demographic theory

Innovation

• Applies Cohort-Component Method for forecasts
• Links forecast uncertainty to demographic theory
• Compares results with Bayesian-based UN forecasts