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Exploring the Correlation and Interplay between the Chinese Agricultural Industry and Economic Influencing Factors: A Study Utilizing Backpropagation (BP) Neural Network and Time Series Analysis

In: Proceedings of the 2023 International Conference on Economic Management, Financial Innovation and Public Service (EMFIPS 2023)

Author

Listed:
  • Yangzheng Qing

    (The Pennsylvania State University State College, College of Liberal Arts)

Abstract

Agriculture's vital role in the economy impacts food supply and consumption. This study uses the Multiple Linear Regression Model (MLR) and the Backpropagation Neural Network (MLP). MLR emphasizes fertilizer, arable land, and precipitation's roles in grain production and consumption, highlighting the need for sustainable practices. MLP reveals complex relationships between economic variables and wheat futures prices. China's Food Consumer Price Index negatively correlates with wheat futures, while the USD to CNY exchange rate shows a positive correlation, emphasizing global trade dynamics. Shanghai Security and Equity of Agricultural Theme Index (SSEAT) robustly correlates with wheat futures, highlighting the impact of agricultural business on prices. Integrated strategies harmonizing agricultural, monetary, and trade policies are crucial for stability.

Suggested Citation

  • Yangzheng Qing, 2024. "Exploring the Correlation and Interplay between the Chinese Agricultural Industry and Economic Influencing Factors: A Study Utilizing Backpropagation (BP) Neural Network and Time Series Analysis," Advances in Economics, Business and Management Research, in: Peng Dou & Keying Zhang (ed.), Proceedings of the 2023 International Conference on Economic Management, Financial Innovation and Public Service (EMFIPS 2023), pages 574-587, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-441-9_49
    DOI: 10.2991/978-94-6463-441-9_49
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