On account of the enormous role of various government policies, it is very important and valuable to find remarkable independent variables from many practical factors of influencing American economy via choosing suitable methods and effective "optimal" models or equations. Based on the American economic data in practice, in order to creatively analyze and predict American economy under two different government policies, we analyze the relationship between each factor and the economy and obtain some greater impact factors to American gross domestic product (in short, GDP) by using a class of stepwise regression, and then we give optimal analysis and predict the influence to American GDP via employing support vector machine models. Further, we establish data comparison to provide beneficial reference for choosing from two different government policies. Finally, we propose some work for future research.
Citation: Yong-xin Lin, Xue Chen, Heng-you Lan. Analysis and prediction of American economy under different government policy based on stepwise regression and support vector machine modelling[J]. Data Science in Finance and Economics, 2023, 3(1): 1-13. doi: 10.3934/DSFE.2023001
On account of the enormous role of various government policies, it is very important and valuable to find remarkable independent variables from many practical factors of influencing American economy via choosing suitable methods and effective "optimal" models or equations. Based on the American economic data in practice, in order to creatively analyze and predict American economy under two different government policies, we analyze the relationship between each factor and the economy and obtain some greater impact factors to American gross domestic product (in short, GDP) by using a class of stepwise regression, and then we give optimal analysis and predict the influence to American GDP via employing support vector machine models. Further, we establish data comparison to provide beneficial reference for choosing from two different government policies. Finally, we propose some work for future research.
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