《我国财政收入影响因素分析》班级：姓名：学号：指导教师：完成时间：摘要：对我国财政收入影响因素进行了定量分析，建立了数学模型，并提出了提高我国财政收入质量的政策建议。

关键词：财政收入实证分析影响因素一、引言财政收入对于国民经济的运行及社会发展具有重要影响。

首先，它是一个国家各项收入得以实现的物质保证。

一个国家财政收入规模大小往往是衡量其经济实力的重要标志。

其次，财政收入是国家对经济实行宏观调控的重要经济杠杆。

宏观调控的首要问题是社会总需求与总供给的平衡问题，实现社会总需求与总供给的平衡，包括总量上的平衡和结构上的平衡两个层次的内容。

财政收入的杠杆既可通过增收和减收来发挥总量调控作用，也可通过对不同财政资金缴纳者的财政负担大小的调整，来发挥结构调整的作用。

此外，财政收入分配也是调整国民收入初次分配格局，实现社会财富公平合理分配的主要工具。

在我国，财政收入的主体是税收收入。

因此，在税收体制及政策不变的情况下，财政收入会随着经济繁荣而增加，随着经济衰退而下降。

我国的财政收入主要包括税收、国有经济收入、债务收入以及其他收入四种形式，因此，财政收入会受到不同因素的影响。

从国民经济部门结构看，财政收入又表现为来自各经济部门的收入。

财政收入的部门构成就是在财政收入中，由来自国民经济各部门的收入所占的不同比例来表现财政收入来源的结构，它体现国民经济各部门与财政收入的关系。

我国财政收入主要来自于工业、农业、商业、交通运输和服务业等部门。

因此，本文认为财政收入主要受到总税收收入、国内生产总值、其他收入和就业人口总数的影响。

二、预设模型令财政收入Y（亿元）为被解释变量，总税收收入X1（亿元）、国内生产总值X2（亿元）、其他收入X3（亿元）、就业人口总数为X4（万人）为解释变量，据此建立回归模型。

二、数据收集从《2010中国统计年鉴》得到1990--2009年每年的财政收入、总税收收入、国内生产总值工、其他收入和就业人口总数的统计数据如下：obs 财政收入Y 总税收收入X1 国内生产总值X2 其他收入X3 就业人口总数X4 1990 2937.1 2821.86 18667.8 299.53 64749 1991 3149.48 2990.17 21781.5 240.1 65491 1992 3483.37 3296.91 26923.5 265.15 66152 1993 4348.95 4255.3 35333.9 191.04 66808 1994 5218.1 5126.88 48197.9 280.18 67455 1995 6242.2 6038.04 60793.7 396.19 68065 1996 7407.99 6909.82 71176.6 724.66 68950 1997 8651.14 8234.04 78973 682.3 69820 1998 9875.95 9262.8 84402.3 833.3 70637 1999 11444.08 10682.58 89677.1 925.43 71394 2000 13395.23 12581.51 99214.6 944.98 72085 2001 16386.04 15301.38 109655.2 1218.1 73025 2002 18903.64 17636.45 120332.7 1328.74 73740 2003 21715.25 20017.31 135822.8 1691.93 74432 2004 26396.47 24165.68 159878.3 2148.32 75200 2005 31649.29 28778.54 184937.4 2707.83 75825 2006 38760.2 34804.35 216314.4 3683.85 76400 2007 51321.78 45621.97 265810.3 4457.96 76990 2008 61330.35 54223.79 314045.4 5552.46 774802009 68518.3 59521.59 340506.9 7215.72 77995三、模型建立1、散点图分析2、单因素或多变量间关系分析Y X1 X2 X3 X4Y 1 0.9989134611478530.9934790452908040.8770144886795640.983602719841508X1 0.998913461147853 10.9937402677184690.8556377347447820.984935296593492X2 0.9934790452908040.993740267718469 10.8561835802284710.986241165680459X3 0.8770144886795640.8556377347447820.856183580228471 10.810940334650381X4 0.9836027198415080.9849352965934920.9862411656804590.810940334650381 1由散点图分析和变量间关系分析可以看出被解释变量财政收入Y与解释变量总税收收入X1、国内生产总值X2、其他收入X3、就业人口总数X4呈线性关系，因此该回归模型设为：μβββββ+++++=443322110X X X X Y3、 模型预模拟由eviews 做ols 回归得到结果：Dependent Variable: Y Method: Least Squares Date: 11/14/11 Time: 17:51 Sample: 1990 2009 Included observations: 20Variable Coefficient Std. Error t-Statistic Prob. C 7299.523 1691.814 4.314614 0.0006 X1 1.062802 0.021108 50.34972 0.0000 X2 0.001770 0.004528 0.391007 0.7013 X3 0.873369 0.119806 7.289852 0.0000 X4-0.1159750.026580-4.3631600.0006R-squared 0.999978 Mean dependent var 20556.75 Adjusted R-squared 0.999972 S.D. dependent var 19987.03 S.E. of regression 106.6264 Akaike info criterion 12.38886 Sum squared resid 170537.9 Schwarz criterion 12.63779 Log likelihood -118.8886 F-statistic 166897.9 Durbin-Watson stat1.496517 Prob(F-statistic)0.0000004321115975.0873369.0001770.0062802.1523.7299X X X X Y -+++=(4.314614) ( 50.34972 ) ( 0.391007) ( 7.289852) ( -4.363160)999978.02=R 999972.02=R 9.166897=F 496517.1.=W D四、 模型检验 1.计量经济学意义检验 ⑴多重共线性检验与解决求相关系数矩阵，得到：Correlation MatrixY X1 X2 X3 X4 1 0.998913461147853 0.9934790452908040.8770144886795640.9836027198415080.998913461110.99374026770.85563773470.984935296547853 18469 44782 934920.993479045290804 0.993740267718469 10.8561835802284710.9862411656804590.877014488679564 0.8556377347447820.856183580228471 10.8109403346503810.983602719841508 0.9849352965934920.9862411656804590.810940334650381 1发现模型存在多重共线性。

接下来运用逐步回归法对模型进行修正:①将各个解释变量分别加入模型,进行一元回归:作Y与X1的回归，结果如下:Dependent Variable: YMethod: Least SquaresDate: 11/22/11 Time: 23:02Sample: 1990 2009Included observations: 20Variable Coefficient Std. Error t-Statistic Prob.C -755.6610 145.2330 -5.203094 0.0001X1 1.144994 0.005760 198.7931 0.0000R-squared 0.999545 Mean dependent var 20556.75Adjusted R-squared 0.999519 S.D. dependent var 19987.03S.E. of regression 438.1521 Akaike info criterion 15.09765Sum squared resid 3455590. Schwarz criterion 15.19722Log likelihood -148.9765 F-statistic 39518.70Durbin-Watson stat 0.475046 Prob(F-statistic) 0.000000作Y与X2的回归，结果如下:Dependent Variable: YMethod: Least SquaresDate: 11/22/11 Time: 23:06Sample: 1990 2009Included observations: 20Variable Coefficient Std. Error t-Statistic Prob.C -5222.077 861.2067 -6.063674 0.0000X2 0.207689 0.005548 37.43267 0.0000R-squared 0.987317 Mean dependent var 20556.75 Adjusted R-squared 0.986612 S.D. dependent var 19987.03 S.E. of regression 2312.610 Akaike info criterion 18.42478 Sum squared resid 96267005 Schwarz criterion 18.52435 Log likelihood -182.2478 F-statistic 1401.205 Durbin-Watson stat 0.188013 Prob(F-statistic) 0.000000作Y与X3的回归，结果如下:Dependent Variable: YMethod: Least SquaresDate: 11/22/11 Time: 23:08Sample: 1990 2009Included observations: 20Variable Coefficient Std. Error t-Statistic Prob.C 2607.879 773.9988 3.369358 0.0034X3 10.03073 0.294311 34.08209 0.0000R-squared 0.984740 Mean dependent var 20556.75 Adjusted R-squared 0.983893 S.D. dependent var 19987.03 S.E. of regression 2536.645 Akaike info criterion 18.60971 Sum squared resid 1.16E+08 Schwarz criterion 18.70929 Log likelihood -184.0971 F-statistic 1161.589 Durbin-Watson stat 1.194389 Prob(F-statistic) 0.000000作Y与X4的回归，结果如下:Dependent Variable: YMethod: Least SquaresDate: 11/22/11 Time: 23:08Sample: 1990 2009Included observations: 20Variable Coefficient Std. Error t-Statistic Prob.C -272959.3 37203.65 -7.336894 0.0000X4 4.097403 0.518467 7.902918 0.0000R-squared 0.776276 Mean dependent var 20556.75 Adjusted R-squared 0.763846 S.D. dependent var 19987.03 S.E. of regression 9712.824 Akaike info criterion 21.29492 Sum squared resid 1.70E+09 Schwarz criterion 21.39449 Log likelihood -210.9492 F-statistic 62.45611 Durbin-Watson stat 0.157356 Prob(F-statistic) 0.000000②依据可决系数最大的原则选取X1作为进入回归模型的第一个解释变量,再依次将其余变量分别代入回归得:作Y与X1、X2的回归，结果如下Dependent Variable: YMethod: Least SquaresDate: 11/22/11 Time: 23:09Sample: 1990 2009Included observations: 20Variable Coefficient Std. Error t-Statistic Prob.C -188.4285 239.0743 -0.788159 0.4415X1 1.281594 0.049472 25.90568 0.0000X2 -0.025055 0.009029 -2.774908 0.0130R-squared 0.999687 Mean dependent var 20556.75Adjusted R-squared 0.999650 S.D. dependent var 19987.03S.E. of regression 374.0345 Akaike info criterion 14.82405Sum squared resid 2378330. Schwarz criterion 14.97341Log likelihood -145.2405 F-statistic 27118.20Durbin-Watson stat 0.683510 Prob(F-statistic) 0.000000 作Y与X1、X3的回归，结果如下Dependent Variable: YMethod: Least SquaresDate: 11/22/11 Time: 23:10Sample: 1990 2009Included observations: 20Variable Coefficient Std. Error t-Statistic Prob.C -351.1054 83.15053 -4.222527 0.0006X1 0.992813 0.018707 53.07196 0.0000X3 1.356936 0.165109 8.218410 0.0000R-squared 0.999908 Mean dependent var 20556.75Adjusted R-squared 0.999898 S.D. dependent var 19987.03S.E. of regression 202.1735 Akaike info criterion 13.59361Sum squared resid 694859.9 Schwarz criterion 13.74297Log likelihood -132.9361 F-statistic 92839.33Durbin-Watson stat 1.177765 Prob(F-statistic) 0.000000作Y与X1、X4的回归，结果如下Dependent Variable: YMethod: Least SquaresDate: 11/22/11 Time: 23:10Sample: 1990 2009Included observations: 20Variable Coefficient Std. Error t-Statistic Prob.C 11853.46 1824.522 6.496748 0.0000X1 1.185886 0.006645 178.4608 0.0000X4 -0.186645 0.026984 -6.917003 0.0000R-squared 0.999881 Mean dependent var 20556.75Adjusted R-squared 0.999867 S.D. dependent var 19987.03S.E. of regression 230.8464 Akaike info criterion 13.85886Sum squared resid 905931.0 Schwarz criterion 14.00822Log likelihood -135.5886 F-statistic 71206.90Durbin-Watson stat 1.459938 Prob(F-statistic) 0.000000③在满足经济意义和可决系数的条件下选取X3作为进入模型的第二个解释变量,再次进行回归则:作Y与X1、X3、X2的回归，结果如下Dependent Variable: YMethod: Least SquaresDate: 11/22/11 Time: 23:13Sample: 1990 2009Included observations: 20Variable Coefficient Std. Error t-Statistic Prob.C -76.04458 100.1724 -0.759137 0.4588X1 1.085924 0.029801 36.43881 0.0000X3 1.210853 0.133444 9.073877 0.0000X2 -0.014073 0.003944 -3.567901 0.0026R-squared 0.999949 Mean dependent var 20556.75Adjusted R-squared 0.999939 S.D. dependent var 19987.03S.E. of regression 155.5183 Akaike info criterion 13.10826Sum squared resid 386975.0 Schwarz criterion 13.30741Log likelihood -127.0826 F-statistic 104602.9Durbin-Watson stat 1.196933 Prob(F-statistic) 0.000000作Y与X1、X3、X4的回归，结果如下Dependent Variable: YMethod: Least SquaresDate: 11/22/11 Time: 23:13Sample: 1990 2009Included observations: 20Variable Coefficient Std. Error t-Statistic Prob.C 6781.764 1024.745 6.618003 0.0000X1 1.068642 0.014514 73.62764 0.0000X3 0.891069 0.107949 8.254551 0.0000X4 -0.107639 0.015451 -6.966675 0.0000R-squared 0.999977 Mean dependent var 20556.75Adjusted R-squared 0.999973 S.D. dependent var 19987.03S.E. of regression 103.7654 Akaike info criterion 12.29900Sum squared resid 172276.1 Schwarz criterion 12.49814Log likelihood -118.9900 F-statistic 234970.9Durbin-Watson stat 1.451447 Prob(F-statistic) 0.000000④可见加入其余任何一个变量都会导致系数符号与经济意义不符,故最终修正后的回归模型为:Dependent Variable: YMethod: Least SquaresDate: 11/30/11 Time: 12:18Sample: 1990 2009Included observations: 20Variable Coefficient Std. Error t-Statistic Prob.C -351.1054 83.15053 -4.222527 0.0006X1 0.992813 0.018707 53.07196 0.0000X3 1.356936 0.165109 8.218410 0.0000R-squared 0.999908 Mean dependent var 20556.75Adjusted R-squared 0.999898 S.D. dependent var 19987.03S.E. of regression 202.1735 Akaike info criterion 13.59361Sum squared resid 694859.9 Schwarz criterion 13.74297Log likelihood -132.9361 F-statistic 92839.33Durbin-Watson stat1.177765 Prob(F-statistic)0.00000031356936.1992813.01054.351X X Y ++-=(-4.222527) ( 53.07196) ( 8.218410)999908.02=R 999898.02=R 33.92839=F 177765.1.=W D⑵异方差检验与修正① 图示法ee 与X1的散点图如下：说明ee 与X1存在单调递增型异方差性。