Probabilistic population forecasts for small regions

Julius Goes and Henriette Engelhardt
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Julius Goes: Universität Bamberg
Henriette Engelhardt: Otto-Friedrich-Universität Bamberg

Demographic Research, 2026, vol. 54, issue 23, 719-762

Abstract: Background: Age-specific population forecasts for small areas or subnational regions are a valuable tool for local governments. However, typical population projection methods based on the cohort-component approach are difficult to apply on a smaller subnational scale. Objective: We introduce Bayesian methods suitable for obtaining reliable age-specific population forecasts for small regions using the cohort-component method. Methods: Our approach improves fertility forecasting by extending the Lee–Carter model with an age-region interaction term. We propose to forecast net-migration counts using skewed error terms, and introduce a Dirichlet regression to model migration age patterns as well as age proportions of fertility. Results: We run our model to produce age-specific population forecasts for a set of 13 heterogeneous regions in Bavaria, Germany. We compare our method with other standard approaches and find that it produces superior out-of-sample forecasts according to both point measures and scoring rules. Conclusions: The findings suggest that the proposed Bayesian methods offer good predictive accuracy and are suitable in obtaining precise forecasts of age-specific population for smaller geo-graphical regions. Contribution: We introduce a new method for the probabilistic projection of subnational population that works well and outperforms other current methods.

Keywords: population forecasting; Bayesian modelling; cohort-component method (search for similar items in EconPapers)
JEL-codes: J1 Z0 (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:dem:demres:v:54:y:2026:i:23

DOI: 10.4054/DemRes.2026.54.23

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