Affiliations: [a] College of Public Administration and Law, Liaoning Technical University, Fuxin, Liaoning, China
| [b] Department of Government Policy, Graduate School, University of Chinese Academy of Social Sciences, Beijing, China
Correspondence:
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Corresponding author. Shuai Rong, and Xiaodan Wang, College of Public Administration and Law, Liaoning Technical University, 123000 Fuxin, Liaoning, China. Department of Government Policy, Graduate School, Chinese Academy of Social Sciences, 102488 Beijing, China. E-mails: ll19824855030@163.com (Shuai Rong) and rehou2003@163.com (Xiaodan Wang).
Abstract: The consumer price index (CPI) is an important indicator to measure inflation or deflation, which is closely related to residents’ lives and affects the direction of national macroeconomic policy formulation. It is a common method to discuss CPI from the perspective of economic analysis, but the statistical principles and influencing factors related to CPI are often ignored. Thus, the impact of different types of CPI on China’s overall CPI was discussed from three aspects: statistical simulation, machine learning prediction and correlation analysis of various types of influencing factors and CPI in this study. Realistic data from the National Bureau of Statistics from 2010 to 2022 were selected as the analysis object. The Statistical analysis showed that in 2015 and 2020, CPI had a fluctuating trend due to the impact of education and transportation. Four types of statistical models including Gauss, Lorentz, Extreme and Pearson were compared. It was determined that the R2 fitted by Extreme model was higher (R2 = 0.81), and the optimal year of simulation was around 2019, which was close to reality. To accurately predict the CPI, the results of Support Vector Machine, Regression decision tree and Gaussian regression (GPR) were compared, and the GPR was determined to be the optimal model (R2 = 0.99). In addition, Spearman matrix analyzed the correlation between CPI and various influencing factors. Herein, this study provided a new method to determine and predict the changing trend of CPI by using big data analysis.
