税收收入影响因素分析.doc

税收收入影响因素分析实验名称：税收收入影响因素分析姓 名： 张凤凤 学 号： 902016112 班 级： 09注税 指导教师： 刘勇 时 间： 二一一年十二月 税收收入影响因素分析十一届三中全会以后，中国的经济一直处于高速增长之中。经济增长的高速发展，势必会影响国家财政政策和国家福利水平。而税收作为国家财政收入中最主要的部分对这些政策的实施也会有很大的影响。近些年来，国家的税收也受到多种因素的影响。所以，这篇文章将以计量经济学的角度分析一下影响我国税收的因素。关键字：税收收入 国内生产总值 商品零售价格指数 进出口总额 一、引言： 经济发展水平决定税收收入水平，税收同时也反作用于经济。要实现经济的持续增长，必须要求与经济紧密关联的税收符合其发展的要求，即政府筹集的税收收入应尽可能的满足其实现职能的需求，同时又不至于损害经济的发展。影响未来的需求，我们需要研究影响中国税收收入的主要原因，分析中央和地方税收收入增长的数量规律，从结构上对税收收入的影响做一个很好的了解，对于预测中国税收未来的增长趋势具有重要的作用，对于我国的社会主义现代化建设具有重要意义。二、模型设定研究影响中国税收未来增长的主要原因，需要考虑以下几个方面的内容：1，对固定资产投资资金来源的衡量，用什么数据来表现呢？我们选用中国税收收入作为被解释变量（y）分析影响中国税收未来增长的主要原因。 2，数据性质的选择。考虑到截面数据受到制约，时间序列数据更加合理，所以本项目选择了1990年到2009年的时间序列数据来建立模型。3，影响因素的分析。从宏观经济看，经济整体增长是税收增长的基本源泉，所以经济整体增长是影响中国税收未来增长的主要原因的主要影响因素，所以选用国内生产总值（GDP）作为经济整体增长水平的代表。除此之外，根据经济理论，还有众多因素会影响中国税收未来增长的主要原因：首先，公共财政的需求。税收收入是财政收入的主体，社会经济的发展和社会保障的完善等都对公共财政提出了要求，因此对预算支出所表现的公共财政的需求（即财政支出）对当年的税收收入可能会产生影响，但是其数据获得比较困难，因为公共财政的需求与财政支出关系密切，所以选择财政支出作为其代表。其次，物价水平。居民的收入水平与物价水平有一定的关系，我们选择商品零售价格指数作为物价水平的代表。再次，进出口总额。进出口的收入水平与税收收入存在一定的联系，所以我们选择进出口总额来作为解释变量。因此，准备将“国内生产总值（）”、“财政支出（）”、“商品零售价格指数（）”、“进出口总额（）”作为解释变量建立模型。4，模型形式的设计 我们将方程形式设定为二次型 然后将影响因素以某种方式引入模型。三、数据的收集本文收集了从1990年到2009年20年的数据，如表所示年份税收收入（Y）/亿元国内生产总值（X1）/亿元财政支出（X2）/亿元商品零售价格指数（X3）/进出口总额（X4）/亿元19902821.8618667.83083.59102.15560.119912990.1721781.53386.62102.97225.819923296.9126923.53742.2105.49119.619934255.335333.94642.3113.21127119945126.8848197.95792.62121.720381.919956038.0460793.76823.72114.823499.919966909.8271176.67937.55106.124133.819978234.04789739233.56100.826967.219989262.884402.310798.1897.426849.7199910682.5889677.113187.679729896.2200012581.5199214.615886.598.539273.2200115301.38109655.218902.5899.242183.6200217636.45120332.722053.1598.751378.2200320017.31135822.824649.9599.970483.5200424165.68159878.328486.89102.895539.1200528778.54184937.433930.28100.8116921.8200634804.35216314.440422.73101140971.4466200745621.97265810.349781.35103.8166740.1884200854223.79314045.462592.66105.9179921.4702200959521.59340506.976299.9398.8150648.0635数据来源：国家统计局10年统计年鉴四、模型的估计与调整方程形式设定为二次型 EVIEWS的最小二乘估计结果为 Dependent Variable: YMethod: Least SquaresDate: 12/21/11 Time: 23:07Sample: 1990 2009Included observations: 20VariableCoefficientStd. Errort-StatisticProb. C-3278.0383482.277-0.9413490.3614X10.0114630.0217940.5259560.6066X20.6168330.0762298.0918460.0000X328.8526931.667380.9111170.3766X40.0623210.0147474.2259460.0007R-squared0.998283 Mean dependent var18613.55Adjusted R-squared0.997825 S.D. dependent var17452.03S.E. of regression813.9629 Akaike info criterion16.45402Sum squared resid9938033. Schwarz criterion16.70296Log likelihood-159.5402 F-statistic2179.867Durbin-Watson stat1.267206 Prob(F-statistic)0.000000经济意义检验：从回归的结果可以看出，国内生产总值（）、财政支出（）、商品零售价格指数（）、进出口总额（）符号均为正，符合经济意义。统计推断检验。该模型R2=0.998283,修正的R2=0.997825，可决系数很高，拟合优度较好，F检验值=2179.867，明显显著。但是当a=0.05时，t a/2(n-k-1)= t a/2(20-7-1)= t 0.025(12)=2.179, x1 x3 的系数t检验不显著，这表明可能存在多重共线性。 相关系数表X1X2X3X4X110.99179395592-0.2811048125370.969178531911X20.991793955921-0.2781042468630.948919334761X3-0.281104812537-0.2781042468631-0.214155020717X40.9691785319110.948919334761-0.2141550207171由相关系数表可以看出，各解释变量之间除了x3之外的相关系数较高，证实确实存在严重的多重共线性。修正多重共线性：运用OLS方法逐一求Y对各个解释变量的回归。结合经济意义和统计检验选出拟合效果最好的一元线性回归方程。Dependent Variable: YMethod: Least SquaresDate: 12/21/11 Time: 23:55Sample: 1990 2009Included observations: 20VariableCoefficientStd. Errort-StatisticProb. C-3927.648663.6284-5.9184440.0000X10.1816050.00427542.476350.0000R-squared0.990122 Mean dependent var18613.55Adjusted R-squared0.989573 S.D. dependent var17452.03S.E. of regression1782.051 Akaike info criterion17.90356Sum squared resid57162682 Schwarz criterion18.00313Log likelihood-177.0356 F-statistic1804.240Durbin-Watson stat0.192210 Prob(F-statistic)0.000000Dependent Variable: YMethod: Least SquaresDate: 12/21/11 Time: 23:56Sample: 1990 2009Included observations: 20VariableCoefficientStd. Errort-StatisticProb. C286.7356497.35000.5765270.5714X20.8299550.01653550.192540.0000R-squared0.992906 Mean dependent var18613.55Adjusted R-squared0.992512 S.D. dependent var17452.03S.E. of regression1510.211 Akaike info criterion17.57253Sum squared resid41053290 Schwarz criterion17.67210Log likelihood-173.7253 F-statistic2519.291Durbin-Watson stat1.279618 Prob(F-statistic)0.000000Dependent Variable: YMethod: Least SquaresDate: 12/21/11 Time: 23:56Sample: 1990 2009Included observations: 20VariableCoefficientStd. Errort-StatisticProb. C91495.3464602.741.4162760.1738X3-703.8998622.8170-1.1301870.2732R-squared0.066260 Mean dependent var18613.55Adjusted R-squared0.014386 S.D. dependent var17452.03S.E. of regression17326.05 Akaike info criterion22.45245Sum squared resid5.40E+09 Schwarz criterion22.55202Log likelihood-222.5245 F-statistic1.277323Durbin-Watson stat0.098220 Prob(F-statistic)0.273233Dependent Variable: YMethod: Least SquaresDate: 12/21/11 Time: 23:57Sample: 1990 2009Included observations: 20VariableCoefficientStd. Errort-StatisticProb. C559.77001482.3310.3776280.7101X40.2914330.01767616.487550.0000R-squared0.937897 Mean dependent var18613.55Adjusted R-squared0.934446 S.D. dependent var17452.03S.E. of regression4468.319 Akaike info criterion19.74205Sum squared resid3.59E+08 Schwarz criterion19.84162Log likelihood-195.4205 F-statistic271.8394Durbin-Watson stat0.719107 Prob(F-statistic)0.000000其中加入x2的方程修正的R2最大，其方程为：Y=286.7356 + 0.829955X2 (0.576527) (50.19254) 修正的R2= 0.992512 S E=1510.211 F=2519.291所以，以x2为基础，顺次加入其他的自变量逐步回归：当a=0.05时，t a/2(n-k-1)= t a/2(20-7-1)= t 0.025(12)=2.179Y= -1543.209 + 0.487335X2 + 0.075696X1 -2.279957 4.712918 3.340826 修正的R2=0.995214 S E= 1207.394 F= 1976.304应为x1的引入改进了修正的R2和F值且其他回归参数的t检验在统计上仍然显著，所以保留x1，方程为： Y= -1543.209 + 0.487335X2 + 0.075696X1继续： Y = -8430.382 + 0.486494X2 + 0.077099X1+ 65.01505X3 -1.803502 4.869821 3.518878 1.487996 修正的R2=0.995533 S E= 1166.458 F= 1412.372式中X3不显著，删去，继续： Y = -144.6187 + 0.624773X2 + 0.007062X1+ 0.065952X4 -0.265949 8.294223 0.334068 4.669806 修正的R2= 0.997848 S E= 809.6306 F= 2937.398X4虽然显著，但是它的引入影响了其他回归参数的估计值的数值，使得随机项和x1的回归参数通不过t检验，所以删去x4所以： Y= -1543.209+ 0.075696X1 + 0.487335X2 -2.279957 4.712918 3.340826 修正的R2=0.995214 S E= 1207.394 F= 1976.304即为最优模型obs税收收入（Y）/亿元国内生产总值（X1）/亿元财政支出（X2）/亿元19902821.8618667.83083.5919912990.1721781.53386.6219923296.9126923.53742.219934255.335333.94642.319945126.8848197.95792.6219956038.0460793.76823.7219966909.8271176.67937.5519978234.04789739233.5619989262.884402.3107985889677.113187.67200012581.5199214.615886.5200115301.38109655.218902.58200217636.45120332.72205331135822.824649.95200424165.68159878.328486.89200528778.54184937.433930.28200634804.35216314.440422.73200745621.97265810.349781.35200854223.79314045.462592.66200959521.59340506.976299.93最小二乘估计为：Dependent Variable: YMethod: Least SquaresDate: 12/22/11 Time: 15:25Sample: 1990 2009Included observations: 20VariableCoefficientStd. Errort-StatisticProb. C-1543.209676.8588-2.2799570.0358X10.0756960.0226583.3408260.0039X20.4873350.1034044.7129180.0002R-squared0.995717 Mean dependent var18613.55Adjusted R-squared0.995214 S.D. dependent var17452.03S.E. of regression1207.394 Akaike info criterion17.16780Sum squared resid24782606 Schwarz criterion17.31716Log likelihood-168.6780 F-statistic1976.304Durbin-Watson stat0.857004 Prob(F-statistic)0.000000残差平方和对解释变量的散点图：由散点图可以看出，残差平方和e2对解释变量x的散点图主要分布在图形中的下三角形，残差平方和e2随解释变量x的变动呈现增大的趋势，所以，模型可能存在异方差。White检验：White Heteroskedasticity Test:F-statistic6.050557 Probability0.003476Obs*R-squared13.67271 Probability0.017828Test Equation:Dependent Variable: RESID2Method: Least SquaresDate: 12/22/11 Time: 15:26Sample: 1990 2009Included observations: 20VariableCoefficientStd. Errort-StatisticProb. C4420714.1170077.3.7781390.0020X1-398.0270123.7104-3.2174100.0062X120.0082460.0026193.1486040.0071X1*X2-0.0719370.023553-3.0542430.0086X21736.983591.84002.9348870.0109X220.1571550.0524732.9949850.0096R-squared0.683636 Mean dependent var1239130.Adjusted R-squared0.570648 S.D. dependent var1774942.S.E. of regression1163029. Akaike info criterion31.01428Sum squared resid1.89E+13 Schwarz criterion31.31300Log likelihood-304.1428 F-statistic6.050557Durbin-Watson stat2.063062 Prob(F-statistic)0.003476从表中可以看出，nR2=20*0.683636=13.67272由white检验知，在a=0.05下，查表得知 (5)= 11.0705，因为nR2=13.67272 (5)= 11.0705，所以拒绝原假设，表明模型中的随机误差存在异方差。修正异方差：使用w5=1/abs（e）作为权数，得出：Dependent Variable: YMethod: Least SquaresDate: 12/22/11 Time: 18:31Sample: 1990 2009Included observations: 20Weighting series: W5VariableCoefficientStd. Errort-StatisticProb. C73.239403.19641422.912990.0000X10.0097450.00028134.729310.0000X20.7913250.002771285.55340.0000Weighted StatisticsR-squared1.000000 Mean dependent var3338.463Adjusted R-squared1.000000 S.D. dependent var14493.59S.E. of regression0.279064 Akaike info criterion0.422729Sum squared resid1.323904 Schwarz criterion0.572089Log likelihood-1.227294 F-statistic1763274.Durbin-Watson stat1.048047 Prob(F-statistic)0.000000Unweighted StatisticsR-squared0.993469 Mean dependent var18613.55Adjusted R-squared0.992701 S.D. dependent var17452.03S.E. of regression1491.022 Sum squared resid37793490Durbin-Watson stat1.274612估计的结果为：y =73.23940 +0.009745x1 + 0.791325x2 （22.91299）（34.7293） (285.5534) R2=0.993469 DW=1.048047 F=1763274.再次做怀特检验：White Heteroskedasticity Test:F-statistic0.559656 Probability0.729260Obs*R-squared3.331625 Probability0.649004Test Equation:Dependent Variable: STD_RESID2Method: Least SquaresDate: 12/22/11 Time: 18:24Sample: 1990 2009Included observations: 20VariableCoefficientStd. Errort-StatisticProb. C52.3387739.959311.3098020.2113X1-0.0012810.004225-0.3032000.7662X124.33E-088.94E-080.4846640.6354X1*X2-4.32E-078.04E-07-0.5369940.5997X20.0083010.0202120.4106760.6875X221.04E-061.79E-060.5808120.5706R-squared0.166581 Mean dependent var63.29112Adjusted R-squared-0.131068 S.D. dependent var37.34648S.E. of regression39.71862 Akaike info criterion10.44484Sum squared resid22085.96 Schwarz criterion10.74356Log likelihood-98.44842 F-statistic0.559656Durbin-Watson stat1.527039 Prob(F-statistic)0.729260从表中可以看出，nR2=20*0.166581=3.33162,由white检验知，在a=0.05下，查表得知 (5)= 11.0705，因为nR2=3.33162 (5)= 11.0705，所以不拒绝原假设，表明模型中的随机误差不存在异方差。自相关检验：查表得出上限为1.100，下限为1.537，因为1.100DW=1.2746121.537，所以不能确定是否存在自相关。 修正自相关：使用迭代法修正得到：Dependent Variable: YMethod: Least SquaresDate: 12/22/11 Time: 19:08Sample(adjusted): 1991 2009Included observations: 19 after adjusting endpointsConvergence achieved after 8 iterationsVariableCoefficientStd. Errort-StatisticProb. C-6698.2632196.930-3.0489190.0081X10.1792020.0203608.8016570.0000X20.0663370.0775250.8556810.4056AR(1)0.8174370.0872539.3686100.0000R-squared0.998913 Mean dependent var19444.69Adjusted R-squared0.998696 S.D. dependent var17518.87S.E. of regression632.5987 Akaike info criterion15.92221Sum squared resid6002716. Schwarz criterion16.12104Log likelihood-147.2610 F-statistic4596.579Durbin-Watson stat1.471722 Prob(F-statistic)0.000000Inverted AR Roots .82DW