Articlegreen2013

Reconstructing Past Populations With Uncertainty From Fragmentary\nData

Mark Christopher Wheldon · Adrian E. Raftery · Samuel J. Clark · Patrick Gerland

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Insurance, Mortality, Demography, Risk Management Statistical Methods and Bayesian Inference Census and Population Estimation

Abstract

Current methods for reconstructing human populations of the past by age and sex\nare deterministic or do not formally account for measurement error. We propose a\nmethod for simultaneously estimating age-specific population counts, fertility\nrates, mortality rates, and net international migration flows from fragmentary\ndata that incorporates measurement error. Inference is based on joint posterior\nprobability distributions that yield fully probabilistic interval estimates. It\nis designed for the kind of data commonly collected in modern demographic\nsurveys and censuses. Population dynamics over the period of reconstruction are\nmodeled by embedding formal demographic accounting relationships in a Bayesian\nhierarchical model. Informative priors are specified for vital rates, migration\nrates, population counts at baseline, and their respective measurement error\nvariances. We investigate calibration of central posterior marginal probability\nintervals by simulation and demonstrate the method by reconstructing the female\npopulation of Burkina Faso from 1960 to 2005. Supplementary materials for this\narticle are available online and the method is implemented in the R package\n“popReconstruct.”

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References

2007World Population Prospects The 2006 Revision 2002Demography, Measuring and Modeling Population Processes 2006Uncertain Judgements: Eliciting Experts' Probabilities 1982The Population History of England 1541-1871. A Reconstruction 2006Demographic forecasting: 1980 to 2005 in review 1982The Population History of England 1541-1871. A Reconstruction.1998Probabilistic Approaches to Population Forecasting 1994Old and New Methods in Historical Demography.

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