影响税收的几个因素分析计量经济学论文.doc

金融计量学论文影响税收的几个因素分析班级：金融工程1003学号：0206100304姓名：李瑶成 绩1数据选取（20分）2模型建立与数据分析（40分）3Eviews应用（10分）4结论陈述（10分）5整体行文（20分）6总 分摘要税收是我国财政收入的基本因素，影响着我国的经济发展。本文通过查阅相关网站信息数据对影响我国税收的因素进行论述。通过Eviews计量学软件对国内生产总值、财政支出、商品零售价格指数等税收收入影响因素进行一定的证明与研究分析，得出相关结论，并对我国的税收提一些建议。关键词：税收 Eviews 国内生产总值AbstractThe tax is the basic factors of Chinas fiscal revenue impact on Chinas economic development. This article discusses the factors affecting Chinas tax revenue by access to the relevant site information and data. Some formal research and analysis, the influencing factors of the the Eviews metrology software, the tax revenues of the gross domestic product (GDP), fiscal spending, the retail price index and draw relevant conclusions, and our tax some advice.Key words: tax Eviews GDP序言 税收是国家为实现其职能，凭借政治权力，按照法律规定，通过税收工具强制地、无偿地征收参与国民收入和社会产品的分配和再分配取得财政收入的一种形式。税收主要用于国防和军队建设、国家公务员工资发放、道路交通和城市基础设施建设、科学研究、医疗卫生防疫、文化教育、救灾赈济、环境保护等领域。而税收一方面受经济发展的制约，但同时又对经济宏观发展起到重要作用。因此，我们需要对影响税收的重要因素加以分析。一 变量的选取从整体来看，经济的增长是税收增长的主要源泉。因此，选择国内生产总值作为解释变量x1。税收是财政收入的一个主体，社会经济的发展会对公共财产产生需求。则财政支出可以代表，作为解释变量x2。我国的税制结构以流转税为主，以现行价格计算的GDP等指标和经营者的收入水平都与物价水平有关。所以选取商品零售指数作为物价水平的代表作为变量解释x3。二 数据的选取以下是选取的样本数据，单位均为亿元年份国内生产总值国家财政支出商品零售物价指数税收收入1980 4545.624 1228.830 106.0000 571.70001981 4891.561 1138.410 102.4000 629.89001982 5323.351 1229.980 101.9000 700.02001983 5962.652 1409.520 101.5000 775.59001984 7208.052 1701.020 102.8000 947.35001985 9016.037 2004.250 108.8000 2040.7901986 10275.18 2204.910 106.0000 2090.7301987 12058.62 2262.180 107.3000 2140.3601988 15042.82 2491.210 118.5000 2390.4701989 16992.32 2823.780 117.8000 2727.4001990 18667.82 3083.590 102.1000 2821.8601991 21781.50 3386.620 102.9000 2990.1701992 26923.48 3742.200 105.4000 3296.9101993 35333.92 4642.300 113.2000 4255.3001994 48197.86 5792.620 121.7000 5126.8801995 60793.73 6823.720 114.8000 6038.0401996 71176.59 7937.550 106.1000 6909.8201997 78973.03 9233.560 100.8000 8234.0401998 84402.28 10798.18 97.40000 9262.8001999 89677.05 13187.67 97.00000 10682.582000 99214.55 15886.50 98.50000 12581.512001 109655.2 18902.58 99.20000 15301.382002 120332.7 22053.15 98.70000 17636.452003 135822.8 24649.95 99.90590 20017.312004 159878.3 28486.89 102.8062 25718.002005 183867.9 33930.28 100.7774 30866.002006 210871.0 40422.73 101.0282 37636.00（以上数据来源于中国统计年鉴及中宏数据库）三 试验分析 1、设定的线性回归模型为：Y=+ Dependent Variable: YMethod: Least SquaresDate: 06/02/12 Time: 23:51Sample: 1980 2006Included observations: 27VariableCoefficientStd. Errort-StatisticProb. C-6357.3062589.143-2.4553710.0221X1-0.0111910.014037-0.7972610.4335X20.9670820.07682112.588750.0000X357.1184124.003452.3795920.0260R-squared0.994954 Mean dependent var8681.087Adjusted R-squared0.994296 S.D. dependent var9909.343S.E. of regression748.4057 Akaike info criterion16.20972Sum squared resid12882553 Schwarz criterion16.40170Log likelihood-214.8312 F-statistic1511.718Durbin-Watson stat0.691548 Prob(F-statistic)0.000000由上表可以得出回归方程：Y=-6357.306-0.011191*x1+0.967082*x2+57.11841*x3 2589.143 0.014037 0.076821 24.00345T=-2.455371 -0.797261 12.58875 0.0260 994954.02=R 994295.02=R f=1511.718 四 模型检验1、经济意义检验在假定其他变量不变的情况下，每当国内生产总值增加一亿时，税收便减少0.011191%；每当国家财政支出增加一亿时，税收增加0.967082%；每当商品零售物价指数增加一亿时，税收增加57.11841%。其中我认为国民生产总值与物价零售指数有一定出入，下文会有所校正。2、统计检验拟合优度由表中得出的两个数据（以下两个），可知模型对样本拟合的较好994954.02=R 994295.02=R T检验中三个解释变量的t值分别是t0=-2.455371,t1=-0.797261,t2=12.58875,t3=2.379592.在5%显著性水平下自由度为n-k-1=27-3-1=23的t的临界值t0.025(23)=2.069其中截距的t值小于临界值说明截距与零没有显著性差异，三个偏斜率有一个没有通过显著性检验，t2与t3通过了显著性检验3、多重共线性的检验YX1X2X3Y1.0000000.9797460.996789-0.383615X10.9797461.0000000.984833-0.407265X20.9967890.9848331.000000-0.416781X3-0.383615-0.407265-0.4167811.000000由上图可知x1与x2之间的相关系数高达0.984833，两者高度正相关。将国内生产总值x1对国家财政支出x2进行回归分析Dependent Variable: X1Method: Least SquaresDate: 06/03/12 Time: 19:23Sample: 1980 2006Included observations: 27VariableCoefficientStd. Errort-StatisticProb. X25.3643510.18901328.380900.0000C7063.3492797.1842.5251640.0183R-squared0.969897 Mean dependent var60995.78Adjusted R-squared0.968693 S.D. dependent var60277.90S.E. of regression10665.50 Akaike info criterion21.45860Sum squared resid2.84E+09 Schwarz criterion21.55459Log likelihood-287.6911 F-statistic805.4757Durbin-Watson stat0.144634 Prob(F-statistic)0.000000X1i=7063.349+5.3643512 969897.02=R DW=0.144634 F=805.4757因此，x1与x2之间存在显著地线性关系VIF=1/（1-R2）=33.2192810 因此该模型具有多重共线性多重共线性修正结果分析运用OLS方法逐一求y对各个结束变量的回归Y与x1：y=-1143.176+0.161065x1 R2=0.959902VariableCoefficientStd. Errort-StatisticProb. C-1143.176559.4057-2.0435540.0517X10.1610650.00658424.463690.0000R-squared0.959902 Mean dependent var8681.087Adjusted R-squared0.958298 S.D. dependent var9909.343S.E. of regression2023.592 Akaike info criterion18.13432Sum squared resid1.02E+08 Schwarz criterion18.23031Log likelihood-242.8134 F-statistic598.4724Durbin-Watson stat0.170737 Prob(F-statistic)0.000000Y与x2:y=-292.7317+0.892575x2 R2=0.993589Included observations: 27VariableCoefficientStd. Errort-StatisticProb. X20.8925750.01434062.244310.0000C-292.7317212.2144-1.3794150.1800R-squared0.993589 Mean dependent var8681.087Adjusted R-squared0.993332 S.D. dependent var9909.343S.E. of regression809.1614 Akaike info criterion16.30106Sum squared resid16368556 Schwarz criterion16.39705Log likelihood-218.0643 F-statistic3874.355Durbin-Watson stat0.501126 Prob(F-statistic)0.000000Y与x3:y=68011.85+-564.9916x3 R2=0.147161Dependent Variable: YMethod: Least SquaresDate: 06/03/12 Time: 19:51Sample: 1980 2006Included observations: 27VariableCoefficientStd. Errort-StatisticProb. C68011.8528622.302.3761840.0255X3-564.9916272.0256-2.0769790.0482R-squared0.147161 Mean dependent var8681.087Adjusted R-squared0.113047 S.D. dependent var9909.343S.E. of regression9332.439 Akaike info criterion21.19157Sum squared resid2.18E+09 Schwarz criterion21.28756Log likelihood-284.0862 F-statistic4.313843Durbin-Watson stat0.179687 Prob(F-statistic)0.048232由上面的三个基本回归方程可知，x2是最重要的解释变量，所以选择第二个基本回归方程作为出事的回归模型逐步回归将其余变量逐一代入式y=-292.7317+0.892575x2得出如下几个模型Y x2 x3: y=-6394.656+0.906950x2+56.73074x3 R2=0.994815 994383.02=R DW=0.652300 F=2302.212Dependent Variable: YMethod: Least SquaresDate: 06/03/12 Time: 20:11Sample: 1980 2006Included observations: 27VariableCoefficientStd. Errort-StatisticProb. C-6394.6562568.992-2.4891690.0201X20.9069500.01448062.636270.0000X356.7307423.815652.3820780.0255R-squared0.994815 Mean dependent var8681.087Adjusted R-squared0.994383 S.D. dependent var9909.343S.E. of regression742.7027 Akaike info criterion16.16291Sum squared resid13238574 Schwarz criterion16.30689Log likelihood-215.1993 F-statistic2302.212Durbin-Watson stat0.652300 Prob(F-statistic)0.000000Y x1 x2 x3: Y=-6357.306-0.011191*x1+0.967082*x2+57.11841*x3 994954.02=R 994295.02=R DW=0.652300 F=1511.718 通过以上分析，得出x1税收影响并不显著，故将其剔除。在删除X1后模型的统计检验有较大改善，经过以上分析，Y对X2、X3的回归模型较优。最终回归结果如下：y=-6394.656+0.906950x2+56.73074x3 R2=0.994815 994383.02=R DW=0.652300 F=2302.2124、异方差性y=-6394.656+0.906950x2+56.73074x3 由G-Q检验，对样本按x2由大到小排序，去除中间的4个样本，剩余22个样本，再分成两个样本容量为11的子样本，对两个子样本分别用OLS法做回归子样本1：Dependent Variable: YMethod: Least SquaresDate: 06/03/12 Time: 20:58Sample: 1980 1990Included observations: 11VariableCoefficientStd. Errort-StatisticProb. C-20728.7112657.86-1.6376150.1401X20.9368950.03107530.149240.0000X3191.9785126.60511.5163570.1679R-squared0.991411 Mean dependent var17713.26Adjusted R-squared0.989263 S.D. dependent var9994.315S.E. of regression1035.583 Akaike info criterion16.95032Sum squared resid8579459. Schwarz criterion17.05883Log likelihood-90.22675 F-statistic461.6999Durbin-Watson stat0.713244 Prob(F-statistic)0.000000Y=-20728.71+0.936895x2+191.9785x3R2=0. 0.991411 RSS1=8579459子样本2：Dependent Variable: YMethod: Least SquaresDate: 06/03/12 Time: 21:14Sample: 1996 2006Included observations: 11VariableCoefficientStd. Errort-StatisticProb. C-2531.9821207.246-2.0973210.0692X21.2134650.11085010.946950.0000X316.5979012.252481.3546570.2125R-squared0.960384 Mean dependent var1621.469Adjusted R-squared0.950480 S.D. dependent var895.8196S.E. of regression199.3467 Akaike info criterion13.65497Sum squared resid317912.7 Schwarz criterion13.76349Log likelihood-72.10233 F-statistic96.97020Durbin-Watson stat1.934652 Prob(F-statistic)0.000002Y=-2531.982+1.213465x2+16.59790x3R2=0.960384 RSS2=317912.7计算F统计量：F=RSS1/(11-2-1) RSS2/(11-2-1)=26.9868在5%的显著性水平下，自由度为（8，8）的f的分布临界值为F0.005（8,8）=3.44，于是拒绝了同方差的假设，表明元模型存在异方差。异方差性修正结果分析采用加权最小二乘法进行估计：以1/|ei|为权重进行加权最小二乘法，则有Dependent Variable: YMethod: Least SquaresDate: 06/03/12 Time: 21:37Sample(adjusted): 1996 2006Included observations: 11 after adjusting endpointsWeighting series: 1/ABS(E1