Research on multivariate statistical analysis methods of rural economic dynamics in the context of digital countryside

Predicting dynamic changes in the rural economy is an important step in maintaining the stability of the rural economy, based on econometric models, data quantification of dynamic changes in the rural economy facilitates a clearer understanding of the changes in the rural economy, which helps to improve the accuracy of rural economic research. This paper takes the GDP data of a village from January 2013 to December 2013 for a total of 12 months as the research object for empirical analysis. A multiple regression model is established to explore the influencing factors of the dynamic changes of the rural economy, and then combined with the VAR model to design the method of measuring the dynamic changes of the rural economy. Then we do unit root test for each variable time series, and determine the time sequence of variable changes and the response of the explanatory variables through Granger causality test, impulse response function, and variance decomposition under the guarantee of model smoothness. Finally, according to the results of the model to explain the dynamic changes in the rural economy, to achieve the analytical effect of predicting the dynamic changes in the rural economy. Through the impulse response analysis using the VAR model, it can be analyzed that the first-level agricultural products, farmers’ consumption, agricultural production materials and money supply have a certain pulling effect on the gross rural product. It can be concluded that intra-rural investment has the greatest impact on the rural economy.