Analysis and Prediction of Total Retail Sales of Consumer Goods Based on Multiple Regression and SARIMA Model
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In order to explore the influencing factors and prediction methods of total retail sales of consumer goods under the new normal of economy, this paper studies the time series data of China from 2007 to 2018, analyzes the influence of multiple economic variables on total retail sales of consumer goods by using multiple regression model, and uses seasonal autoregressive moving average model (SARIMA) to make high-precision fitting. The basis is provided for its short-term prediction, and finally the policy suggestions are given. The results show that the stock of money and quasi money (M2) and the state budget expenditure are positively correlated with the total retail sales of consumer goods; the total retail sales of social consumer goods in China will maintain a rapid growth trend in the future.
Total retail sales of consumer goods; Multiple regression; SARIMA model; Time series