18: Hierarchical Bayesian Copula Model for Probabilistic Population Projection

Thomas M. Menino Convention & Exhibition Center 

Population forecasts inform critical decisions in public policy and economic planning, yet existing state-of-the-art methods often underestimate predictive uncertainty by modeling key demographic variables, such as fertility rates and life expectancy, as independent stochastic processes. This work proposes a hierarchical Bayesian framework for probabilistic population forecasting that models the joint dynamics of fertility and mortality across countries, regions, and time. Dependence structure will be represented using copulas, which allow flexible joint modeling while preserving interpretable marginal structures. The hierarchical design enables partial pooling across countries and regions, allowing countries with sparse data to be partially informed by broader regional patterns of demographic change while retaining country-specific trajectories. By producing more faithful joint predictive uncertainty quantification, this work delivers uncertainty-aware population projections that better support evidence-based policy and equitable decision-making. The proposed methods will be illustrated using simulations and a data analysis.

Bayesian Hierarchical Model

Dependence Modeling

Probabilistic Forecasting

Copula

Bayesian Demography

Applied Statistics 

Section on Bayesian Statistical Science