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<p>This study systematically investigates the determinants of CCEC economic growth using both static and dynamic panel regression methods and employs stepwise regression to select the optimal model. Building on this foundation, a nonlinear panel analysis approach is introduced by constructing a PSTR model. Firstly, the static panel analysis results reveal that key variables—LABOR, FAI, FDI, FE, and IV—significantly promote CCEC’s economic growth. Additionally, fixed asset investment and innovation input serve as vital drivers of growth, also demonstrating positive contributions. Compared to the static model, the dynamic panel results show FE and IV are identified as the main dynamic drivers of CCEC’s growth, all exhibiting positive effects, thereby confirming the validity and explanatory power of the dynamic model. Finally, results indicate that IV constitutes the key threshold variable currently shaping economic growth in the region. As innovation input increases, the marginal contributions of other economic variables to growth gradually diminish, demonstrating clear diminishing returns. When regional innovation investment exceeds RMB 970&#xa0;million, economic growth enters a steady-state phase with slowing growth rates, implying that policy focus should shift towards optimizing innovation models and policies rather than blindly expanding investment, which could otherwise lead to counterproductive outcomes.</p>

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CCEC’S Regional economic development: determinant identification, optimal modeling, and pstr analysis

  • 期刊论文

  • 发表日期：

    2026年3月12日

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  • Yong Yin

摘要

This study systematically investigates the determinants of CCEC economic growth using both static and dynamic panel regression methods and employs stepwise regression to select the optimal model. Building on this foundation, a nonlinear panel analysis approach is introduced by constructing a PSTR model. Firstly, the static panel analysis results reveal that key variables—LABOR, FAI, FDI, FE, and IV—significantly promote CCEC’s economic growth. Additionally, fixed asset investment and innovation input serve as vital drivers of growth, also demonstrating positive contributions. Compared to the static model, the dynamic panel results show FE and IV are identified as the main dynamic drivers of CCEC’s growth, all exhibiting positive effects, thereby confirming the validity and explanatory power of the dynamic model. Finally, results indicate that IV constitutes the key threshold variable currently shaping economic growth in the region. As innovation input increases, the marginal contributions of other economic variables to growth gradually diminish, demonstrating clear diminishing returns. When regional innovation investment exceeds RMB 970 million, economic growth enters a steady-state phase with slowing growth rates, implying that policy focus should shift towards optimizing innovation models and policies rather than blindly expanding investment, which could otherwise lead to counterproductive outcomes.