School of Management of Science and Engineering, Anhui University of Finance and Economic, Bengbu, 233030, China
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.
Ridge regression, Lasso regression, multicollinearity, financial revenue
Nie Ruichao. Analysis of influencing factors of Fiscal revenue in Beijing based on Ridge regression and Lasso regression model. International Journal of New Developments in Engineering and Society (2022) Vol.6, Issue 2: 1-5. https://doi.org/10.25236/IJNDES.2022.060201.
 Bian Yunuo, Research on the Influence of China's Fiscal Revenue based on ANN-MLP model. Economic Research Guide,2022(06):67-69.]
 Lu W J, A study on inter-governmental fiscal revenue distribution and its Impact in China [D]. Zhejiang university, 2021. DOI: 10.27461 /, dc nki. Gzidx. 2021.002691. E
 Qin Quan. Analysis and prediction of influencing factors of Fiscal Revenue in Hunan Province based on Python software. The Chinese market, 2021 (29): 40 and 41. DO1:10.13939 / j.carol carroll nki. ZGSC. 2021.29.040.
 Ke Li. The dynamic analysis of local finance income gap of our country [D]. Chinese finance research institute, 2021. The DOI: 10.26975 /, dc nki. GCCKS. 2021.000057.
 Li Luxiu, Dong Xiyuan, Empirical analysis of influencing factors of China's fiscal revenue based on EViews. China Collective Economy,2021(16):92-94.]
 Zhang Yingchun, resource tax reform's influence on the fiscal revenue in shandong province research [D]. Shandong university of finance and economics, 2021, DOI: 10.27274 /, dc nki. GSDJC. 2021.000410.
 NiJie, shandong finance income factors and forecast analysis [D]. Shandong normal university, 2021. The DOI: 10.27280 /, dc nki. Gsdsu. 2021.001185.
 Zhao Hui, Correlation analysis between Fiscal Revenue and GDP in Liangcheng County . Inner Mongolia Science & Technology and Economy,2021(08):67-68.
 Wang Ran, Li Tingshan, Zhan Xiaoyu, Empirical Analysis of Factors Affecting China's Fiscal Revenue [C]// 2021 International Conference on Innovative Talent Training and Sustainable Development proceedings (Chinese).. 2021:77-79. DOI: 10.26914 / Arthur c. nkihy. 2021.011622.
 Xie Yi and Analysis on the impact of Guangdong's Foreign Trade import and Export on Fiscal Revenue . Economic Forum,2021(03):46-55.