Multivariate VAR system of regional economic growth under big data

Traditional research has problems such as limited data samples, limited variable selection, and insufficient real-time analysis capabilities. With the advent of the big data era, researchers are increasingly using big data analysis to discover the laws of development. This paper combines the VAR system with big data analysis technology to study regional economic growth variables, simulate economic changes in region A, and use the Markov chain Monte Carlo algorithm and historical simulation method to analyze the process of industrial change. The model accuracy of the two algorithms is evaluated by the historical data observation loss value of the VAR post-test. The results show that at a confidence level of 0.95, the risk prediction of the historical simulation method is more accurate. The study also found that adding additional variables to the VAR system will lead to model information loss and an increase in parameters.