Population Projection of India using decennial time series data: A Bayesian Study

Anurag Verma¹ , Abhinav Singh² , Gyan Prakash Singh³ , Pramendra Singh Pundir⁴

Section:Research Paper, Product Type: Journal-Paper
Vol.7 , Issue.5 , pp.24-30, Oct-2020

Online published on Oct 31, 2020

Copyright © Anurag Verma, Abhinav Singh, Gyan Prakash Singh, Pramendra Singh Pundir . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
This paper explores the use of Markov chain Monte Carlo simulation technique in the Bayesian inference through WinBUGS software to project the future population of India using the logistics growth model under the Bayesian framework. The purpose of this study is to illustrate the Bayesian approach to population forecasting. It also summarizes the benefits of this approach. In addition to this new and possibly improved form of logistics growth model for population, projections has been proposed in the Bayesian setup. This model was featured in the historical time series from 1901 to 2011 and is used to predict future population data till the year 2101. These results are compared to the latest projections made in other studies. These studies have shown that our projections provide a stable and narrow credible interval accordingly. This paper concludes with some conclusions and suggestions for future work.

Key-Words / Index Term :
Population forecasting; Bayesian forecasting; Highest posterior density; non-linear regression model; Monte Carlo error

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