Uncertainty in population projections: the state of the art

Abstract

In this paper I critically review the state of the art in population projections, focusing on how uncertainty is handled in three approaches: the classical cohort-component, the frequentist probabilistic model and the Bayesian paradigm. Next, I focus on recent developments on mortality, fertility and migration projections under the Bayesian setting, which have been clearly at the frontier of knowledge in demography. By evaluating the merits and limitations of each framework, I conclude that in the near future the Bayesian paradigm will offer the most promising approach to population projections, since it combines expert opinion, information that demographers have readily available from their empirical analyses and sophisticated statistical and  computational methods to deal with uncertainty. Hence, the availability of population forecasts that take uncertainty carefully into account may enhance communication among demographers by allowing for greater flexibility in reflecting demographic beliefs.