Proceedings of the 5th International Conference on Economic Management and Model Engineering, ICEMME 2023, November 17–19, 2023, Beijing, China

Research Article

Prediction of GDP in the BRICS Countries Based on the Proportions of Industry and Agriculture

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  • @INPROCEEDINGS{10.4108/eai.17-11-2023.2342697,
        author={Ziqi  Guo and Yonghao  Li},
        title={Prediction of GDP in the BRICS Countries Based on the Proportions of Industry and Agriculture},
        proceedings={Proceedings of the 5th International Conference on Economic Management and Model Engineering, ICEMME 2023, November 17--19, 2023, Beijing, China},
        publisher={EAI},
        proceedings_a={ICEMME},
        year={2024},
        month={2},
        keywords={multiple regression analysis; gdp; brics countries; economic structure},
        doi={10.4108/eai.17-11-2023.2342697}
    }
    
  • Ziqi Guo
    Yonghao Li
    Year: 2024
    Prediction of GDP in the BRICS Countries Based on the Proportions of Industry and Agriculture
    ICEMME
    EAI
    DOI: 10.4108/eai.17-11-2023.2342697
Ziqi Guo1, Yonghao Li1,*
  • 1: Wuhan University
*Contact email: 395665106@qq.com

Abstract

In the realm of gross domestic product(GDP) research, the multiple regression model has garnered significant academic attention due to its ability to comprehensively consider various factors and its exceptional predictive performance. This study explores multidimensional GDP-related data and introduces a novel model to explain GDP, focusing on its relationship with the proportion of a country's industrial and agricultural output. To analyze the interaction between a nation's economic condition and its economic structure, we employ both static and dynamic research methods. We first decompose GDP into four components and select five countries with similar economic development stages for our analysis. We then use GDP as a measure of economic growth and conduct an in-depth analysis by creating scatter plots illustrating the relationship between the proportion of agriculture and industry in GDP. Finally, we construct a multivariate nonlinear regression model to examine the relationship between GDP and the proportion of industrial and agricultural output. The research findings reveal that our proposed GDP-industrial and agricultural proportion model has undergone rigorous significance testing and demonstrates strong explanatory capability.