Analysis of the Economic Impact of the US Presidential Candidates on the United States and China
DOI:
https://doi.org/10.54691/bcpbm.v18i.584Keywords:
stepwise regression, SVM, BP neural networkAbstract
The year 2020 is the year of the US presidential election, with both candidates focusing on key development areas such as finance and trade, economic and financial governance. Different candidates will shape different global economic and financial development strategies that will have a greater impact on the U.S. economy and the global economy, including China’s. China-us relations have become the focus of the world's attention. We collected data for both China and the US (2002-2019) based on data regression and prediction. Besides, the model of random forest and support vector machine is established to analyze the relationship between various data to help us solve the problem and to think about the impact of different candidates' election on the Chinese and American economies. The clearer the data relationship is, the more accurate our conclusions will be. This paper looks up the relevant data and selects the features through stepwise regression, and uses the final retained features in SVM model to fit the relationship between various measures and economic development, and finally makes prediction. This paper establishes a BP neural network model to fit China's economic development data with the quantitative data of the relevant measures of the president of the United States, and finally realize the prediction of China's economic development. based on the results of the previous two questions, this paper compares Biden's and Trump's policies towards China, and gives relevant suggestions.
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