Analysis of influencing factors of Fiscal revenue in Beijing based on Ridge regression and Lasso regression model

Abstract

: Based on the fiscal revenue and other relevant economic index data of Beijing from 1995 to 2020, this study uses the research methods of Ridge regression and Lasso regression to explore the influencing factors of Beijing’s fiscal revenue. Considering that the traditional linear regression model will produce strong multicollinearity among many variables. Therefore, ridge regression and Lasso regression model were firstly used to reduce the influence of multicollinearity between variables, and then variable selection was carried out. Finally, the two models were compared according to the analysis results, and the optimal analysis model was selected. The results show that compared with ridge regression model, lasso regression model has better goodness of fit, smaller error and better model. The added value of the second industry, power generation, resident population, urban per capita disposable income and total retail sales of social consumer goods has a positive impact on fiscal revenue, and the whole social fixed assets investment, employment in cities and towns, per capita consumption expenditure of urban households is has a certain negative impact on fiscal income level.