Population Forecasts for Bangladesh, Using a Bayesian Methodology

Authors

  • Md Mahsin Institute of Statistical Research and Training; University of Dhaka, Dhaka 1000
  • Syed Shahadat Hossain Institute of Statistical Research and Training; University of Dhaka, Dhaka 1000

DOI:

https://doi.org/10.3329/jhpn.v30i4.13331

Keywords:

Cohort component Method, Monte Carlo error, Gompertz model, Highest posterior density, Logistic model, Markov Chain Monte Carlo, Non-linear regression model, Population projection, WinBUGS

Abstract

Population projection for many developing countries could be quite a challenging task for the demographers mostly due to lack of availability of enough reliable data. The objective of this paper is to present an overview of the existing methods for population forecasting and to propose an alternative based on the Bayesian statistics, combining the formality of inference. The analysis has been made using Markov Chain Monte Carlo (MCMC) technique for Bayesian methodology available with the software WinBUGS. Convergence diagnostic techniques available with the WinBUGS software have been applied to ensure the convergence of the chains necessary for the implementation of MCMC. The Bayesian approach allows for the use of observed data and expert judgements by means of appropriate priors, and a more realistic population forecasts, along with associated uncertainty, has been possible.

DOI: http://dx.doi.org/10.3329/jhpn.v30i4.13331

J HEALTH POPUL NUTR 2012 Dec;30(4):456-463

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Published

2013-01-15

How to Cite

Mahsin, M., & Hossain, S. S. (2013). Population Forecasts for Bangladesh, Using a Bayesian Methodology. Journal of Health, Population and Nutrition, 30(4), 456–463. https://doi.org/10.3329/jhpn.v30i4.13331

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Section

Original Papers