Analysis of factors influencing regional economic expansion based on OOB coefficients under RF algorithm
Yue Xu, Zhi Wei Cao, Muyuan Wang · BCP Business & Management · 2022
In the context of China's economic growth, the economic situation of national new areas is particularly important, and it is of great significance to study its economic expansion factors. In this paper, the regression random forest algorithm (RF) is used and the economic panel data of Shanghai, China from 2011-2022 is selected for algorithm learning, which obtained algorithm with low error and high accuracy. After the algorithm was learned, the OOB coefficients of the influencing factors were obtained and analyzed to study their impact on regional economic inflation. The results show that the per capita disposable income and the ratio of the added value of the tertiary industry to the GDP play an important role in economic expansion. Finally, policy recommendations for regional economic development are offered, which can help address regional economic risks and contribute to regional economic improvement and growth. The research in this paper analyses the importance of factors influencing regional economic growth by using machine learning methods and quarterly and annual provincial panel data, making the conclusions drawn more innovative and robust.