Bayesian Population Projections with Model Uncertainty

In this paper, we develop a Bayesian cohort population projection model that incorporates model uncertainty. We first argue that a Bayesian approach is a more natural framework for incorporating various forms of uncertainty in probabilistic projections. Second, we demonstrate the differences that arise from choosing different Lee-Carter type models for fertility, mortality, immigration and emigration in terms of forecasted age patterns and their associated measures of uncertainty. Third, we incorporate this information into a cohort component projection model and use Bayesian model averaging techniques to produce a model-averaged population forecast for the United Kingdom by age and sex. We end the paper by discussing the merits and flexibility of a Bayesian cohort component projection model and highlight some areas where this work could be extended.