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Bayesian Population Projections for the United Nations.

Adrian E Raftery1, Leontine Alkema2, Patrick Gerland3

  • 1Department of Statistics, Box 354322, University of Washington, Seattle, WA 98195-4322. raftery@uw.edu ; Web: www.stat.washington.edu/raftery .

Statistical Science : a Review Journal of the Institute of Mathematical Statistics
|October 18, 2014
PubMed
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This study introduces a Bayesian method for probabilistic population projections, offering uncertainty estimates unlike current UN methods. This approach enhances demographic forecasting for global population data.

Area of Science:

  • Demography
  • Statistical Modeling
  • Bayesian Inference

Background:

  • The United Nations (UN) provides standard deterministic population projections, widely used globally.
  • Current UN projections lack uncertainty quantification, a critical limitation for many applications.
  • Deterministic methods fail to capture the inherent variability in demographic processes.

Purpose of the Study:

  • To develop and describe a Bayesian methodology for probabilistic population projections.
  • To enable the United Nations to produce population projections with uncertainty estimates.
  • To address limitations in current UN projection assumptions, particularly regarding long-term fertility.

Main Methods:

  • Utilizes Bayesian hierarchical models as the core of the projection framework.
Keywords:
Bayesian hierarchical modelLeslie matrixcohort component projection methoddouble logistic functionlife expectancytotal fertility rate

Related Experiment Videos

  • Focuses on modeling key demographic parameters: total fertility rate and life expectancy at birth.
  • Implements the method using a suite of R packages: bayesTFR, bayesLife, bayesPop, and bayesDem.

    • Main Results:

    • Demonstrates a feasible Bayesian approach for generating probabilistic population projections for numerous countries.
    • Illustrates the method's application and potential extensions for refining demographic forecasts.
    • Provides a framework that can incorporate nuanced assumptions about future fertility trends.

    Conclusions:

    • The proposed Bayesian method offers a significant advancement over standard deterministic population projections.
    • This approach provides crucial uncertainty quantification, enhancing the reliability of demographic forecasts.
    • The R package implementation facilitates the adoption of probabilistic projections by organizations like the UN.