A comprehensive analysis of digital inclusive finance's influence on high quality enterprise development through fixed effects and deep learning frameworks
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View abstract on PubMed
Summary
This summary is machine-generated.Digital Inclusive Finance (DIF) enhances Total Factor Productivity (TFP) for high-quality enterprise development. Advanced deep learning models, including KAN and GNN, significantly improve nonlinear relationship prediction compared to traditional methods.
Area Of Science
- Economics
- Financial Technology
- Data Science
Background
- High-quality enterprise development (HQED) is vital for economic growth, driven by Total Factor Productivity (TFP).
- Digital Inclusive Finance (DIF) is a key enabler of HQED, yet its complex relationship with TFP requires advanced modeling.
- Traditional econometric models struggle to capture nonlinear dynamics inherent in financial and economic data.
Purpose Of The Study
- To investigate the nonlinear relationship between Digital Inclusive Finance (DIF) and Total Factor Productivity (TFP).
- To enhance the prediction accuracy of economic variables by exploring advanced deep learning techniques.
- To compare the performance of novel deep learning models against traditional time series forecasting methods.
Main Methods
- Initial analysis using double fixed-effects models with robustness and heterogeneity tests.
- Application of deep learning models including Kolmogorov-Arnold Network (KAN), Graph Neural Network (GNN), Transformer, LSTM, BiLSTM, and GRU.
- Statistical evaluation using paired t-tests and Cohen's d effect size to assess prediction error metrics.
Main Results
- Double fixed-effects models confirmed linear relationships but lacked predictive power for nonlinear dynamics.
- Deep learning models, particularly those incorporating KAN and GNN, demonstrated superior performance in capturing nonlinearities.
- The advanced models significantly improved prediction accuracy and reduced errors compared to traditional forecasting methods.
Conclusions
- Digital Inclusive Finance (DIF) exhibits complex, nonlinear effects on Total Factor Productivity (TFP).
- Integrating KAN and GNN into time series forecasting substantially enhances the ability to model and predict these economic relationships.
- Advanced deep learning approaches offer a powerful toolkit for understanding and forecasting economic transformations driven by financial innovation.
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