Exploring New Models for Population Prediction in Detecting
Demographic Phase Change
by Arindam Gupta, Sabyasachi Bhattacharya ,Asis Kumar Chattyopadhyay.
Logistic model has long history regarding it's usefulness in
population predictions. But the model has some limitations when
applying for the sparse census data sets, typically available for
developing countries. In such situation the relative growth rates
(RGR) exhibit some unusual trend (increasing, primary increasing and
then decreasing) which is different from the common decreasing trend
of logistic law. To tackle those complicated demographic situations
we have successfully explored a simplified version of Tsoularis and
Wallace model (TWM) which can explain all of these feasible
monotonic structures of RGR. In addition to this we have also
proposed another model (PM) by assuming RGR as a direct function of
time covariate but not the size. The model has some key advantages
than the simplified TWM (STWM). It can detect the demographic phase
change point at which the developing country switches over towards
developed one. We performed RGR modelling (as a function of time)
but not the size as neither TWM nor STWM is analytically solvable
and the underlying population model is better identifiable in the
former case but not in the later. The less number of parameters
involve in both the STWM and PM ensure a better chance of
convergence under non-linear least square estimation than the
original TWM with more parameters.
Population prediction, relative
growth rate, Tsoularis-Wallace model, demographic phase change
Arindam Gupta, firstname.lastname@example.org
Sabyasachi Bhattacharya, email@example.com
Asis Kumar Chattyopadhyay, firstname.lastname@example.org
Kishore K Das,email@example.com
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