通过固定效应和深度学习框架,全面分析数字包容性金融对高质量企业发展的影响
Dedai Wei1, Zimo Wang2, Hanfu Kang3
1College of Economics, Shenyang University, Shenyang, 110000, China.
Scientific reports
|August 17, 2025
概括
数字包容性金融 (DIF) 提高了企业高质量发展的总因子生产率 (TFP). 先进的深度学习模型,包括KAN和GNN,与传统方法相比,显著改善了非线性关系预测.
科学领域:
- 经济学 经济学 经济学
- 金融技术 金融技术
- 数据科学数据科学数据科学
背景情况:
- 高质量的企业发展 (HQED) 对经济增长至关重要,由总要素生产率 (TFP) 推动.
- 数字包容性金融 (DIF) 是HQED的关键推动者,但其与TFP的复杂关系需要先进的建模.
- 传统的经济计量模型难以捕捉金融和经济数据固有的非线性动态.
研究的目的:
- 研究数字包容性金融 (DIF) 和总因子生产率 (TFP) 之间的非线性关系.
- 通过探索先进的深度学习技术,提高经济变量的预测准确度.
- 将新型深度学习模型的性能与传统时间序列预测方法进行比较.
主要方法:
- 使用双重固定效应模型进行初始分析,并进行稳定性和异质性测试.
- 深度学习模型的应用,包括科尔摩戈罗夫-阿诺德网络 (KAN),图形神经网络 (GNN),变压器,LSTM,BiLSTM和GRU.
- 使用配对的t测试和科恩的d效应大小来评估预测错误指标的统计评估.
主要成果:
- 双固定效应模型证实了线性关系,但缺乏对非线性动态的预测能力.
- 深度学习模型,特别是那些结合KAN和GNN的模型,在捕捉非线性方面表现出卓越的表现.
- 与传统预测方法相比,先进的模型显著提高了预测准确度,并减少了错误.
结论:
- 数字包容性金融 (DIF) 对总因子生产率 (TFP) 产生复杂的非线性影响.
- 将KAN和GNN集成到时间序列预测中,大大提高了模拟和预测这些经济关系的能力.
- 先进的深度学习方法为理解和预测由金融创新驱动的经济转型提供了强大的工具包.
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