Analysis And Prediction of Influencing Factors of Total Retail Sales of Consumer Goods in China——Based on Multiple Linear Regression Models and ARIMA Models

Abstract

This paper takes the annual data of total retail sales of consumer goods in China from 2001 to 2020 as the research object. Using methods such as principal component estimation, ridge regression and Lasso regression, a multivariate linear regression model is established, and the regression results of the three models are compared and analyzed, and the significant factors affecting the total retail sales of consumer goods in China are the income level of residents, the level of consumption of residents, and the development of the tertiary industry, and the less significant factors are demographic factors. In addition, this paper takes the annual data of china's total retail sales of social consumer goods from 1973 to 2020 as the research object, and uses the ALAMA (0,1,1) model to predict and analyze the total retail sales of social consumer goods in China in the next ten years, and concludes that the total retail sales of social consumer goods in China will gradually rise from 2021 to 2030, which is consistent with the actual situation. Thus, it puts forward rationalization suggestions for increasing the total retail sales of consumer goods in China and promoting economic development.