Probability Projection of Total Fertility Rate for Assesing the Replacement Level of Fertility of Selected State Using MCMC Technique under Bayesian Modeling

Abhishek Singh Chauhan¹ , Subhi Srivastava² , Anurag Verma³

Section:Research Paper, Product Type: Journal-Paper
Vol.6 , Issue.6 , pp.14-21, Dec-2019

CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v6i6.1421

Online published on Dec 31, 2019

Copyright © Abhishek Singh Chauhan, Subhi Srivastava, Anurag Verma . 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 :
The present paper explains Bayesian methodological approach for projection of Total fertility rate (TFR) for seven states of India taken as (Uttar Pradesh, Bihar, Assam, Madhya Pradesh, Gujarat, Rajasthan, Haryana). The projection has been done by using Gompertz model and the parameters of the model have been estimated using MCMC (Monte Carlo Markove Chain) Technique in Bayesian procedure. Previous year SRS (Sample Registration System) data for TFR is used since 1981 to 2016 for each state. Non-informative prior distribution for estimating parameter in Bayesian approach was used and the entire analysis was done by using WinBUGS (Bayesian Inference using Gibbs Sampling for Windows) software. As a result of our study the projection model shows that Uttar Pradesh will reach to its replacement level in year 2028, Bihar in 2036, Assam in 2018, Gujarat in 2016, Rajasthan in 2025, whereas Haryana has already reached to its replacement level.

Key-Words / Index Term :
Gompertz Curve, Bayesian Methodology, Total Fertility Rate, Non-informative prior, WinBUGS

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