Projection of Age-Specific Fertility Rates of India using MCMC technique in the Bayesian Inference

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

Section:Research Paper, Product Type: Isroset-Journal
Vol.6 , Issue.2 , pp.34-42, Apr-2019

CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v6i2.3442

Online published on Apr 30, 2019

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 :
In this paper, is an attempt to expand the framework of the population projections developed in early studies (World Population Prospect (2012); Mahsin & Hossain (2012)). In Population projection, the two main component, first, the Total Fertility Rate and second, Life Expectancy at Birth. In early studies these components is projected in the future, then converted into Age-Specific Fertility Rate and Age-Specific Mortality Rate after that combine these two components into well-known Cohort-component methods to project population. This study is attempt to summarize the advantages and disadvantages of this approach. The main purpose of this study is design to project future level of fertility by age under Bayesian approach to measure uncertainty in the future. This approach is illustrating in the Indian context from 1971 to 2012 and is later used to project the ASFR levels in the future to 2061.

Key-Words / Index Term :
Bayesian modelling; Markov chain Monte Carlo; Total Fertility Rates; World Population Prospects; Bayesian forecasting; non-linear regression model

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