基于数据挖掘技术的浙江省财政收入影响因素分析

Analysis of Influencing Factors of Fiscal Revenue in Zhejiang Province Based on Data Mining Technology

摘要: 财政是政府实现其职能的基础，承担着资源整合、资源再分配以及宏观经济调控的职能。与此同时，财政也是社会经济发展水平的重要体现。由此可见，提高财政收入的预测精度对国家、地方来说意义重大。为了提升浙江省财政收入的预测精度，我们以R语言为编程工具，首先通过最优子集法、向前逐步回归法、向后逐步回归法、岭回归及Lasso法分别对浙江省财政收入的影响因素进行分析，得到了5种回归模型并通过它们各自的均方根误差(RMSE)来评估其回归效果。最后，选取Lasso回归模型为最优回归模型。其中，影响浙江省财政收入的关键性因素为：旅游创汇收入、城镇单位就业人员平均工资、第三产业与第二产业产值比、全部金融机构人民币存款余额这四项指标。

Abstract: Finance is the basis of governments to perform their functions whose basic responsibilities are the resource integration, resource reallocation and macroeconomic regulation and control. Besides, finance reflects the level of the development of social and economic to a great degree. This is why, to our country, enhancing the predict accuracy of finance means a lot. In order to accomplish this task, we analyzed the factors of influencing Zhejiang Province’s fiscal revenue, used best subset selection, forward stepwise selection, backward stepwise selection, ridge regression and Lasso regression respectively by using R software. We also give evaluation efficiency of each model by using root-mean-square errors. Finally, we find that the Lasso regression model is the optimal regression model, which can pick the key factors affecting the financial income of Zhejiang province for the four balances: tourism earned foreign exchange earnings, the average wage of urban employees employed, the ratio of the third industry to the second industry, and the RMB deposits of all financial institutions.