TY  - JOUR
A1  - Goes, Julius
A1  - Engelhardt, Henriette
T1  - Probabilistic population forecasts for small regions
Y1  - 2026/04/14
JF  - Demographic Research
JO  - Demographic Research
SN  - 1435-9871
SP  - 719
EP  - 762
DO  - 10.4054/DemRes.2026.54.23
VL  - 54
IS  - 23
UR  - https://www.demographic-research.org/volumes/vol54/23/
L1  - https://www.demographic-research.org/volumes/vol54/23/54-23.pdf
L2  - https://www.demographic-research.org/volumes/vol54/23/54-23.pdf
N2  - 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. 

ER  -