Impact of financial development and internet use on export growth: New evidence from machine learning models
Ahmed Ismail Shahin,
Anis Omri () and
Research in International Business and Finance, 2022, vol. 61, issue C
The current study empirically examines the impact of financial sector development and Internet use on exports value for thirty Chinese provinces from 2000 to 2018 using the Panel-Corrected Standard Error (PCSE) estimation method and Gaussian Process Regression (GPR) machine-learning model. The PCSE method shows that (i) the Internet use increases China's exports; (ii) the impact of the Internet on export growth is larger in high-middle developed provinces; (iii) Internet use increases exports in high-middle developed provinces; however, it has less contribution to exports in low-developing China's provinces; (iv) financial development does not influence export value in the three panels. We also find that the GPR machine-learning model is more robust in predicting the exports growth in China based on the financial development and Internet use, which shows that population, Internet use, GDP, and financial development are the most important factors to predict exports growth in China.
Keywords: Financial development; Internet use; Export; Machine-learning models (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:riibaf:v:61:y:2022:i:c:s0275531922000319
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