Modeling the financial drivers of China’s digital economy: Implications for sustainable growth

This study examines the relationship between financial drivers and GDP growth within China’s digital economy from 2012 to 2021. Using Ordinary Least Squares (OLS) regression, the analysis investigates seven financial indicators: Broad Money (BM), Foreign Direct Investment (FDI), Market Capitalization (MC), Domestic Credit to the Private Sector (DCPS), Research and Development Expenditure (R&D), Gross Capital Formation (GCF), and High-Technology Exports (HTE). While the model explains a substantial proportion of the variance in GDP growth (R 2 = 0.943), the F-test suggests that the predictors, when considered together, are not statistically significant at the 5% level (p = 0.187). This is primarily due to a small sample size (n = 10) and severe multicollinearity, especially for variables like DCPS (VIF = 105), which complicates the interpretation of individual coefficients. Robustness checks using shrinkage methods (Ridge and Lasso), Principal Component Regression (PCR), Partial Least Squares (PLS), and Leave-One-Year-Out cross-validation confirm the presence of multicollinearity and small sample issues. Despite these challenges, year-by-year analysis reveals that liquidity or credit variables (BM, DCPS) influence short-term GDP growth, while innovation or trade indicators (R&D, HTE) support long-term economic transformation. The findings underscore the importance of digital finance in China’s economic development, although the study’s limitations suggest caution when generalizing the results. Future research should extend the analysis with a larger dataset and additional predictors to further validate the relationships between financial indicators and GDP growth.