The United Nations publishes updated estimates and projections of the populations of all the world's countries, broken down by age and sex. These are widely used by international organizations, governments, the private sector and researchers, for example for climate modeling and for assessing progress towards the Millenium Development Goals. The UN's current projections are deterministic, but assessing uncertainty about population estimates and projections is important for policy-making and other purposes. We propose to develop a fully probabilistic population projection methodology. We will develop methods for probabilistic projection of fertility and mortality, taking account of within-country and between-country correlations. We will develop methods for probabilistic projection of international migration. We will develop methods for probabilistic population projections in countries with generalized sexually transmitted infectious disease epidemics, which require special methods because the demographic impact of such diseases is massive and different from most other diseases, being concentrated among the least vulnerable parts of the population, namely young sexually active adults. We will develop methods for reconstructing past populations with uncertainty from fragmentary data. We will produce publicly available software for implementing the new methods.
Every two years, the United Nations produces projections of the populations of all countries, which are widely used by international organizations, governments, the private sector and researchers. The UN's current projections are deterministic, but assessing uncertainty about future population is important for policy-making and other purposes. We propose to develop a fully probabilistic population methodology which will be applicable to all countries, and will also be useful for assessing global concerns, such as climate change and progress towards the Millenium Development Goals.
|Wheldon, Mark C; Raftery, Adrian E; Clark, Samuel J et al. (2016) Bayesian population reconstruction of female populations for less developed and more developed countries. Popul Stud (Camb) 70:21-37|
|Scrucca, Luca; Fop, Michael; Murphy, T Brendan et al. (2016) mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models. R J 8:289-317|
|Onorante, Luca; Raftery, Adrian E (2016) Dynamic Model Averaging in Large Model Spaces Using Dynamic Occam's Window. Eur Econ Rev 81:2-14|
|Friel, Nial; Rastelli, Riccardo; Wyse, Jason et al. (2016) Interlocking directorates in Irish companies using a latent space model for bipartite networks. Proc Natl Acad Sci U S A 113:6629-34|
|Azose, Jonathan J; Å evÄÃkovÃ¡, Hana; Raftery, Adrian E (2016) Probabilistic population projections with migration uncertainty. Proc Natl Acad Sci U S A 113:6460-5|
|Wheldon, Mark C; Raftery, Adrian E; Clark, Samuel J et al. (2015) Bayesian Reconstruction of Two-Sex Populations by Age: Estimating Sex Ratios at Birth and Sex Ratios of Mortality. J R Stat Soc Ser A Stat Soc 178:977-1007|
|Maltiel, Rachael; Raftery, Adrian E; McCormick, Tyler H et al. (2015) Estimating Population Size Using the Network Scale Up Method. Ann Appl Stat 9:1247-1277|
|Bao, Le; Raftery, Adrian E; Reddy, Amala (2015) Estimating the Sizes of Populations At Risk of HIV Infection From Multiple Data Sources Using a Bayesian Hierarchical Model. Stat Interface 8:125-136|
|Azose, Jonathan J; Raftery, Adrian E (2015) Bayesian Probabilistic Projection of International Migration. Demography 52:1627-50|
|Bijak, Jakub; Alberts, Isabel; Alho, Juha et al. (2015) Letter to the Editor: Probabilistic population forecasts for informed decision making. J Off Stat 31:537-544|
Showing the most recent 10 out of 42 publications