Population projections have traditionally been done deterministically using the cohort component method, yielding a single value for each projected future population quantity of interest.  Recently, the United Nation Population Division adopted a probabilistic approach to project fertility, mortality and population for all countries. In this approach, the total fertility rate and female and male life expectancy at birth are projected using Bayesian hierarchical models estimated via Markov Chain Monte Carlo. They are then combined with a cohort component model which yields probabilistic projection for any quantity of interest. The methodology is implemented in a suite of R packages which has been used by the UN analysts producing the most recent revision of the World Population Prospects.

This course will teach the theory and practice behind the UN probabilistic projections. Ideas of the Bayesian hierarchical modeling for the two main components, fertility and mortality, will be explained. In hands-on exercises, students will become familiar with the functionality of the R packages. By the end of the course, they will have a basic understanding of the methods, be able to generate projections using their own data, and visualize probabilistic projections for many quantities of interest using various output formats, such as graphs, tables, maps, and pyramids.

For more information, download the workshop agenda.