Wei Dedai, Wang Zimo, Kang Hanfu, Sha Xinye, Xie Yiran, Dai Anqi, Ouyang Kaichen
College of Economics, Shenyang University, Shenyang, 110000, China.
Department of Philosophy, Nankai University, Tianjin, 300381, China.
Sci Rep. 2025 Aug 17;15(1):30095. doi: 10.1038/s41598-025-14610-y.
In the context of global economic transformation, high-quality enterprise development (HQED) is crucial for driving economic growth, particularly through enhancing Total Factor Productivity (TFPLP). Digital Inclusive Finance (DIF), as a classical financial model, plays an important role in promoting high-quality enterprise development. To explore the relationship between TFP and DIF, we first applied traditional double fixed-effects models, along with robustness and heterogeneity tests, for modeling experiments. This series of tests effectively revealed the theoretical linear relationships between economic variables. However, the double fixed-effects model has limitations in capturing nonlinear relationships and making predictions. Given the growing body of research on existing hybrid models, we acknowledge the importance of exploring and contributing to this evolving area. To address this issue, based on the results of traditional economic analysis, we introduced improved time series models. These advanced deep learning models allow us to better capture the complex nonlinear relationship between DIF and TFP. The experiment initially explored the preliminary structural relationship between DIF and TFP using double fixed-effects models combined with robustness and heterogeneity tests. Then, based on the results of these tests, we selected deep learning features and combined Kolmogorov-Arnold Neural Network (KAN), Graph Neural Network (GNN) models with classic time series deep learning models (Transformer, LSTM, BiLSTM, GRU) to capture the latent nonlinear features in the data for prediction. The results show that, compared to traditional time series forecasting methods, the improved deep learning models perform better in capturing the nonlinear relationships of economic variables, improving prediction accuracy, and reducing prediction errors. Finally, paired t-tests and Cohen's d effect size tests were used to evaluate error metrics, and the results indicate that the introduction of KAN and GNN models significantly improved the performance of time series forecasting models.
在全球经济转型的背景下，高质量企业发展对于推动经济增长至关重要，特别是通过提高全要素生产率（TFPLP）来实现。数字普惠金融（DIF）作为一种经典的金融模式，在促进高质量企业发展方面发挥着重要作用。为了探究全要素生产率与数字普惠金融之间的关系，我们首先应用传统的双重固定效应模型，并进行稳健性和异质性检验，以进行建模实验。这一系列检验有效地揭示了经济变量之间的理论线性关系。然而，双重固定效应模型在捕捉非线性关系和进行预测方面存在局限性。鉴于现有混合模型的研究不断增加，我们认识到探索并为这一不断发展的领域做出贡献的重要性。为了解决这个问题，基于传统经济分析的结果，我们引入了改进的时间序列模型。这些先进的深度学习模型使我们能够更好地捕捉数字普惠金融与全要素生产率之间复杂的非线性关系。实验最初使用双重固定效应模型结合稳健性和异质性检验来探究数字普惠金融与全要素生产率之间的初步结构关系。然后，基于这些检验的结果，我们选择深度学习特征，并将柯尔莫哥洛夫 - 阿诺德神经网络（KAN）、图神经网络（GNN）模型与经典的时间序列深度学习模型（Transformer、LSTM、BiLSTM、GRU）相结合，以捕捉数据中的潜在非线性特征进行预测。结果表明，与传统的时间序列预测方法相比，改进后的深度学习模型在捕捉经济变量的非线性关系、提高预测准确性和减少预测误差方面表现更好。最后，使用配对t检验和科恩效应量d检验来评估误差指标，结果表明引入KAN和GNN模型显著提高了时间序列预测模型的性能。
