《我国财政收入影响因素分析》计量经济学论文eviews分析要点.doc

《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X419902937.12821.8618667.8299.536474919913149.482990.1721781.5240.16549119923483.373296.9126923.5265.156615219934348.954255.335333.9191.046680819945218.15126.8848197.9280.186745519956242.26038.0460793.7396.196806519967407.996909.8271176.6724.666895019978651.148234.0478973682.36982019989875.959262.884402.3833.370637199911444.0810682.5889677.1925.4371394200013395.2312581.5199214.6944.9872085200116386.0415301.38109655.21218.173025200218903.6417636.45120332.71328.7473740200321715.2520017.31135822.81691.9374432200426396.4724165.68159878.32148.3275200200531649.2928778.54184937.42707.8375825200638760.234804.35216314.43683.8576400200751321.7845621.97265810.34457.9676990200861330.3554223.79314045.45552.4677480200968518.359521.59340506.97215.7277995三、模型建立1、散点图分析050000100000150000200000250000300000350000010000300005000070000YX1X2X3X42、单因素或多变量间关系分析YX1X2X3X4Y10.9989134611478530.9934790452908040.8770144886795640.983602719841508X10.99891346114785310.9937402677184690.8556377347447820.984935296593492X20.9934790452908040.99374026771846910.8561835802284710.986241165680459X30.8770144886795640.8556377347447820.85618358022847110.810940334650381X40.9836027198415080.9849352965934920.9862411656804590.8109403346503811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：443322110XXXXY3、模型预模拟由eviews做ols回归得到结果：DependentVariable:YMethod:LeastSquaresDate:11/14/11Time:17:51Sample:19902009Includedobservations:20VariableCoefficientStd.Errort-StatisticProb.C7299.5231691.8144.3146140.0006X11.0628020.02110850.349720.0000X20.0017700.0045280.3910070.7013X30.8733690.1198067.2898520.0000X4-0.1159750.026580-4.3631600.0006R-squared0.999978Meandependentvar20556.75AdjustedR-squared0.999972S.D.dependentvar19987.03S.E.ofregression106.6264Akaikeinfocriterion12.38886Sumsquaredresid170537.9Schwarzcriterion12.63779Loglikelihood-118.8886F-statistic166897.9Durbin-Watsonstat1.496517Prob(F-statistic)0.0000004321115975.0873369.0001770.0062802.1523.7299XXXXY(4.314614)(50.34972)(0.391007)(7.289852)(-4.363160)999978.02R999972.02R9.166897F496517.1.WD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：CorrelationMatrixYX1X2X3X410.9989134611478530.9934790452908040.8770144886795640.9836027198415080.998913461110.99374026770.85563773470.9849352965478531846944782934920.9934790452908040.99374026771846910.8561835802284710.9862411656804590.8770144886795640.8556377347447820.85618358022847110.8109403346503810.9836027198415080.9849352965934920.9862411656804590.8109403346503811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:DependentVariable:YMethod:LeastSquaresDate:11/22/11Time:23:02Sample:19902009Includedobservations:20VariableCoefficientStd.Errort-StatisticProb.C-755.6610145.2330-5.2030940.0001X11.1449940.005760198.79310.0000R-squared0.999545Meandependentvar20556.75AdjustedR-squared0.999519S.D.dependentvar19987.03S.E.ofregression438.1521Akaikeinfocriterion15.09765Sumsquaredresid3455590.Schwarzcriterion15.19722Loglikelihood-148.9765F-statistic39518.70Durbin-Watsonstat0.475046Prob(F-statistic)0.000000作Y与X2的回归，结果如下:DependentVariable:YMethod:LeastSquaresDate:11/22/11Time:23:06Sample:19902009Includedobservations:20VariableCoefficientStd.Errort-StatisticProb.C-5222.077861.2067-6.0636740.0000X20.2076890.00554837.432670.0000R-squared0.987317Meandependentvar20556.75AdjustedR-squared0.986612S.D.dependentvar19987.03S.E.ofregression2312.610Akaikeinfocriterion18.42478Sumsquaredresid96267005Schwarzcriterion18.52435Loglikelihood-182.2478F-statistic1401.205Durbin-Watsonstat0.188013Prob(F-statistic)0.000000作Y与X3的回归，结果如下:DependentVariable:YMethod:LeastSquaresDate:11/22/11Time:23:08Sample:19902009Includedobservations:20VariableCoefficientStd.Errort-StatisticProb.C2607.879773.99883.3693580.0034X310.030730.29431134.082090.0000R-squared0.984740Meandependentvar20556.75AdjustedR-squared0.983893S.D.dependentvar19987.03S.E.ofregression2536.645Akaikeinfocriterion18.60971Sumsquaredresid1.16E+08Schwarzcriterion18.70929Loglikelihood-184.0971F-statistic1161.589Durbin-Watsonstat1.194389Prob(F-statistic)0.000000作Y与X4的回归，结果如下:DependentVariable:YMethod:LeastSquaresDate:11/22/11Time:23:08Sample:19902009Includedobserva