Probabilistic Population Projections with Spatial Correlation

The UN has recently issued official probabilistic population projections for all countries for the first time, based on Bayesian hierarchical models for fertility and mortality. I will describe an extension of the Bayesian hierarchical model that allows for probabilistic projection of aggregate TFR for any set of countries. We model the spatial correlation between country forecast errors as a function of time invariant covariates. This gives improved probabilistic forecasts for regional and other aggregates. Our results suggest that a substantial proportion of the correlation between forecast errors for TFR in different countries is due to countries' geographic proximity to one another, and that if this correlation is accounted for, the quality of probabilitistic projections of TFR for regions and other aggregates is improved. This is joint work with Bailey Fosdick.