Exploring New Models for Population Prediction in Detecting Demographic Phase Change for Sparse Census Data

Abstract

The logistic model has some limitations when applied to the sparse census data sets, typically available for developing countries. In such a situation, the relative growth rates (RGR) exhibit some unusual trends which are different from the common decreasing trend of logistic law. To explain such irregular trends we extend the logistic law by incorporating nonlinear positive and negative feedback terms. We performed RGR modelling as a function of time, as the size covariate model is not analytically solvable and the underlying model is better identifiable in the former case. It can also detect the demographic phase change point of developing country.

Keywords:

• Demographic phase change
• Population prediction
• Relative growth rate

Mathematics Subject Classification:

• 62P25

Acknowledgment

The authors gratefully acknowledge the comments of the referee and the editor, which led to an improved version of the article.