Work

Analysis of influencing factors and trend prediction based on quantile regression and GM(1, 1) model

Year: 2025

Type: article

Abstract: This study adopts the quantile regression method to analyze the influencing factors of low-income, middle-income, and high-income groups at the national, urban, and rural levels in China, respectively, at the quantiles of 0.05, 0.20, 0.50, 0.80, and 0.95. Subsequently, the GM(1, 1) model within the gray-system theory is utilized to predict the proportion of the middle-income group in China in the future. By comparing the prediction results from 2010 to 2017 with the data of the proportion of the middle-income group at the national, urban, and rural levels obtained through kernel density estimation, it is found that the gray-system prediction exhibits high accuracy and satisfactory results. The implications of this study for future social development may lie in providing a certain degree of data reference for social governors.

Source: Journal of Algorithms & Computational Technology

DOI: 10.1177/17483026251331497

Authors: Lianhui Li, Adham Manyara, Jie Liu

Institution: Wenzhou Polytechnic

Language: English

Topic: Grey System Theory Applications

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDGs: Reduced inequalities, No poverty

Open Access status: gold

Estimated APC paid: $2,000