《我国财政收入影响因素分析》计量经济学论文(eviews分析)

1、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

2、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

3、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 X

4、2其他收入 X3就业人口总数 X419902937.12821.8618667.8299.536474919913149.482990.1721781.5240.16549119923483.373296.9126923.5265.156615219934348.954255.335333.9191.046680819945218.15126.8848197.9280.186745519956242.26038.0460793.7396.196806519967407.996909.8271176.6724.666895019978651.148234.0478973682.369820199

5、89875.959262.884402.3833.370637199911444.0810682.5889677.1925.4371394200013395.2312581.5199214.6944.9872085200116386.0415301.38109655.21218.173025200218903.6417636.45120332.71328.7473740200321715.2520017.31135822.81691.9374432200426396.4724165.68159878.32148.3275200200531649.2928778.54184937.42707.8

6、375825200638760.234804.35216314.43683.8576400200751321.7845621.97265810.34457.9676990200861330.3554223.79314045.45552.46774802009 68518.3 59521.59三、 模型建立1、 散点图分析340506.97215.7277995350000300000250000200000150000X1X2X3X4100000500000 100003000050000700002、 单因素或多变量间关系分析YX10.9989134611X20.9934790452X30.

7、8770144886X40.9836027198Y1478539080479564415080.99891346110.99374026770.85563773470.9849352965X14785311846944782934920.99347904520.99374026770.85618358020.9862411656X29080418469128471804590.87701448860.85563773470.85618358020.8109403346X37956444782284711503810.98360271980.98493529650.98624116560.810

8、9403346X4415089349280459503811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：Y 0 1X1 2X 2 3X 3 4X 43、 模型预模拟由 eviews 做 ols 回归得到结果：Dependent Variable: YMethod: Least SquaresDate: 11/14/11Time: 17:51Sample: 1990 2009 Included observations: 20VariableCoefficient

9、Std. Error t-StatisticProb.C7299.5231691.814 4.3146140.0006X11.0628020.021108 50.349720.0000X20.0017700.004528 0.3910070.7013X30.8733690.119806 7.2898520.0000X4-0.1159750.026580 -4.3631600.0006R-squared0.999978Mean dependent var20556.75Adjusted R-squared0.999972S.D. dependent var19987.03S.E. of regr

10、ession106.6264Akaike info criterion12.38886Sum squared resid170537.9Schwarz criterion12.63779Log likelihood-118.8886F-statistic166897.9Durbin-Watson stat1.496517Prob(F-statistic)0.000000Y7299.5231.062802X10.001770X 20.873369X30.115975X 4(4.314614)( 50.34972 )( 0.391007)( 7.289852)( -4.363160)R20.999

11、9782R 0.999972 F166897.9 D.W 1.496517四、 模型检验1.计量经济学意义检验多重共线性检验与解决求相关系数矩阵，得到：Correlation MatrixYX1X2X3X40.99891346110.99347904520.87701448860.98360271981478539080479564415080.998913461110.99374026770.85563773470.9849352965478531846944782934920.99347904520.99374026770.85618358020.986241165690804184691

12、28471804590.87701448860.85563773470.85618358020.81094033467956444782284711503810.98360271980.98493529650.98624116560.8109403346415089349280459503811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: 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

13、2009Included observations: 20VariableCoefficientStd. Error t-StatisticProb.C-755.6610145.2330 -5.2030940.0001X11.1449940.005760 198.79310.0000R-squared0.999545Mean dependent var20556.75Adjusted R-squared0.999519S.D. dependent var19987.03S.E. of regression438.1521Akaike info criterion15.09765Sum squa

14、red resid3455590.Schwarz criterion15.19722Log likelihood-148.9765F-statistic39518.70Durbin-Watson stat0.475046Prob(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 2009 Included observations: 20VariableCoefficientStd. Errort-Statistic

15、Prob.C-5222.077861.2067-6.0636740.0000X20.2076890.00554837.432670.0000R-squared0.987317Mean dependent var20556.75Adjusted R-squared0.986612S.D. dependent var19987.03S.E. of regression2312.610Akaike info criterion18.42478Sum squared resid96267005Schwarz criterion18.52435Log likelihood-182.2478F-stati

16、stic1401.205Durbin-Watson stat0.188013Prob(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 2009 Included observations: 20VariableCoefficientStd. Error t-StatisticProb.C2607.879773.9988 3.3693580.0034X310.030730.294311 34.082090.0000R

17、-squared0.984740Mean dependent var20556.75Adjusted R-squared0.983893S.D. dependent var19987.03S.E. of regression2536.645Akaike info criterion18.60971Sum squared resid1.16E+08Schwarz criterion18.70929Log likelihood-184.0971F-statistic1161.589Durbin-Watson stat1.194389Prob(F-statistic)0.000000作Y 与X4的回

18、归，结果如下 Dependent Variable: Y Method: Least SquaresDate: 11/22/11 Time: 23:08Sample: 1990 2009 Included observations: 20VariableCoefficientStd. Error t-StatisticProb.C-272959.337203.65 -7.3368940.0000X44.0974030.518467 7.9029180.0000R-squared0.776276Mean dependent var20556.75Adjusted R-squared0.76384

19、6S.D. dependent var19987.03S.E. of regression9712.824Akaike info criterion21.29492Sum squared resid1.70E+09Schwarz criterion21.39449Log likelihood-210.9492F-statistic62.45611Durbin-Watson stat0.157356Prob(F-statistic)0.000000 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Depen

20、dent Variable: YMethod: Least SquaresDate: 11/22/11 Time: 23:09Sample: 1990 2009Included observations: 20VariableCoefficientStd. Error t-StatisticProb.C-188.4285239.0743 -0.7881590.4415X11.2815940.049472 25.905680.0000X2-0.0250550.009029 -2.7749080.0130R-squared0.999687Mean dependent var20556.75Adju

21、sted R-squared0.999650S.D. dependent var19987.03S.E. of regression374.0345Akaike info criterion14.82405Sum squared resid2378330.Schwarz criterion14.97341Log likelihood-145.2405F-statistic27118.20Durbin-Watson stat0.683510Prob(F-statistic)0.000000作Y与X1、X3的回归，结果如下Dependent Variable: YMethod: Least Squ

22、aresDate: 11/22/11 Time: 23:10Sample: 1990 2009Included observations: 20VariableCoefficientStd. Error t-StatisticProb.C-351.105483.15053 -4.2225270.0006X10.9928130.018707 53.071960.0000X31.3569360.165109 8.2184100.0000R-squared0.999908Mean dependent var20556.75Adjusted R-squared0.999898S.D. dependen

23、t var19987.03S.E. of regression202.1735Akaike info criterion13.59361Sum squared resid694859.9Schwarz criterion13.74297Log likelihood-132.9361F-statistic92839.33Durbin-Watson stat1.177765Prob(F-statistic)0.000000作Y与X1、X4的回归，结果如下 Dependent Variable: Y Method: Least SquaresDate: 11/22/11 Time: 23:10 Sa

24、mple: 1990 2009Included observations: 20VariableCoefficientStd. Error t-StatisticProb.C11853.461824.522 6.4967480.0000X11.1858860.006645 178.46080.0000X4-0.1866450.026984 -6.9170030.0000R-squared0.999881Mean dependent var20556.75Adjusted R-squared0.999867S.D. dependent var19987.03S.E. of regression2

25、30.8464Akaike info criterion13.85886Sum squared resid905931.0Schwarz criterion14.00822Log likelihood-135.5886F-statistic71206.90Durbin-Watson stat1.459938Prob(F-statistic)0.000000 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

26、/11Time: 23:13Sample: 1990 2009Included observations: 20VariableCoefficientStd. Error t-StatisticProb.C-76.04458100.1724 -0.7591370.4588X11.0859240.029801 36.438810.0000X31.2108530.133444 9.0738770.0000X2-0.0140730.003944 -3.5679010.0026R-squared0.999949Mean dependent var20556.75Adjusted R-squared0.

27、999939S.D. dependent var19987.03S.E. of regression155.5183Akaike info criterion13.10826Sum squared resid386975.0Schwarz criterion13.30741Log likelihood-127.0826F-statistic104602.9Durbin-Watson stat1.196933Prob(F-statistic)0.000000作Y与X1、X3、X4的回归，结果如下Dependent Variable: YMethod: Least SquaresDate: 11/

28、22/11Time: 23:13Sample: 1990 2009Included observations: 20VariableCoefficientStd. Error t-StatisticProb.C6781.7641024.745 6.6180030.0000X11.0686420.014514 73.627640.0000X30.8910690.107949 8.2545510.0000X4-0.1076390.015451 -6.9666750.0000R-squared0.999977Mean dependent var20556.75Adjusted R-squared0.

29、999973S.D. dependent var19987.03S.E. of regression103.7654Akaike info criterion12.29900Sum squared resid172276.1Schwarz criterion12.49814Log likelihood-118.9900F-statistic234970.9Durbin-Watson stat1.451447Prob(F-statistic)0.000000 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:

30、Least SquaresDate: 11/30/11Time: 12:18Sample: 1990 2009Included observations: 20VariableCoefficientStd. Error t-StatisticProb.C-351.105483.15053 -4.2225270.0006X10.9928130.018707 53.071960.0000X31.3569360.165109 8.2184100.0000R-squared0.999908Mean dependent var20556.75Adjusted R-squared0.999898S.D.

31、dependent var19987.03S.E. of regression202.1735Akaike info criterion13.59361Sum squared resid694859.9Schwarz criterion13.74297Log likelihood-132.9361F-statistic92839.33Durbin-Watson stat1.177765 Prob(F-statistic)0.000000Y 351.1054 0.992813X1 1.356936 X 3 (-4.222527)( 53.07196)( 8.218410)22 R2 0.9999

32、08 R 0.999898 F 92839.33 D.W 1.177765异方差检验与修正 图示法 ee与 X1的散点图如下：20000016000012000080000400000 10000 20000 30000 40000 50000 600000X1说明 ee与 X1存在单调递增型异方差性。10.6889310.718727947.5750.000000ee与 X3的散点图如下：200000160000120000800004000000 2000 4000 6000 8000X3说明 ee与 X3存在单调递增型异方差性。 G-Q 检验对 20 组数据剔除掉中间四组剩下的进行分组后

33、，第一组( 1990-1997 )数据的回归结果：Dependent Variable: YMethod: Least SquaresDate: 11/30/11 Time: 12:54Sample: 1990 1997Included observations: 8VariableCoefficientStd. Errort-StatisticProb.X10.9841230.01625560.543200.0000X30.8515180.1566885.4344720.0029C-28.3427545.36993-0.6247030.5596R-squared0.999686Mean de

34、pendent var5179.791Adjusted R-squared0.999560S.D. dependent var2099.840S.E. of regression44.05899Akaike info criterionSum squared resid9705.972Schwarz criterionLog likelihood-39.75573F-statisticDurbin-Watson stat1.663630Prob(F-statistic)残差平方和 RSS1=9705.972第二组( 2002-2009 )数据的回归结果：Dependent Variable:

35、YMethod: Least SquaresDate: 11/30/11 Time: 12:55Sample: 2002 2009Included observations: 8VariableCoefficientStd. Errort-StatisticProb.X11.0664040.02774738.433210.0000X30.8472280.2151143.9385030.0110C-1184.159261.8258-4.5226980.0063R-squared0.999932Mean dependent var39824.41Adjusted R-squared0.999905

36、S.D. dependent var18639.16S.E. of regression182.0047Akaike info criterion13.52594Sum squared resid165628.5Schwarz criterion13.55573Log likelihood-51.10375F-statistic36705.08Durbin-Watson stat1.326122Prob(F-statistic)0.000000残差平方和 RSS2= 165628.5所以 F= RSS2/RSS1= 165628.5/9705.972=17.0646 在给定 =5%下查得临界值

37、 F0.05(4,4) 6.39， F F0.05(4,4) 因此否定两组子样方差相同的假设，从而该总体随机项存在递增 异方差性。 White 方法检验White Heteroskedasticity Test:F-statistic6.142010Probability0.003919Obs*R-squared12.41812Probability0.014498Test Equation:Dependent Variable: RESID2Method: Least SquaresDate: 11/30/11 Time: 13:21Sample: 1990 2009Included obs

38、ervations: 20VariableCoefficientStd. Error t-StatisticProb.C24856.5019211.30 1.2938480.2153X1-20.573277.549127 -2.7252520.0156X120.0002128.04E-05 2.6399820.0186X3237.181378.61323 3.0170670.0087X32-0.0240730.006568 -3.6652300.0023R-squared0.620906Mean dependent var34743.00Adjusted R-squared0.519815S.

39、D. dependent var49156.00S.E. of regression34062.86Akaike info criterion23.92212Sum squared resid1.74E+10Schwarz criterion24.17105Log likelihood-234.2212F-statistic6.142010Durbin-Watson stat1.560937Prob(F-statistic)0.0039192n R2 20 0.620906 12.41812=5%下，临界值 20.05(4) 9.488 拒绝同方差性 修正Dependent Variable:

40、 YMethod: Least SquaresDate: 11/30/11Time: 14:29Sample: 1990 2009Included observations: 20Weighting series: 1/E1VariableCoefficientStd. Errort-StatisticProb.C-314.207443.68550-7.1924860.0000X10.9797580.008622113.63360.0000X31.4572910.06592222.106290.0000Weighted StatisticsR-squared0.999999 Mean depe

41、ndent var27246.27Adjusted R-squared0.999999 S.D. dependent var74471.17S.E. of regression 73.91795 Akaike info criterion 11.58127Sum squared resid92885.67Schwarz criterion11.73063Log likelihood-112.8127F-statistic3138195.Durbin-Watson stat0.956075Prob(F-statistic)0.000000Unweighted StatisticsR-square

42、d0.999902Mean dependent var20556.75Adjusted R-squared0.999891S.D. dependent var19987.03S.E. of regression209.0283Sum squared resid742778.2Durbin-Watson stat1.365483Y 314.20740.979758 X1 1.457291X 3(-7.192486)( 113.6336) ( 22.10629)2R2 0.9999992R 0.999999 F 3138195 D.W 1.365483序列相关性检验从残差项 e2与e2(-1) 及

43、e与时间 t的关系图(如下)看，随机项 呈现正序列相关性。600400200(-120-200-400-600 -400 -200 0 200 400 600E2-600由图可以看出，存在一阶序列相关回归检验残差e2与e2（ -1 ）做回归得：Dependent Variable: EMethod: Least SquaresDate: 12/04/11Time: 15:21Sample (adjusted): 1991 2009Included observations: 19 after adjustmentsVariableCoefficientStd. Error t-Statisti

44、cProb.C16.8152545.69611 0.3679800.7174E(-1)0.3035700.231114 1.3135080.2065R-squared0.092138Mean dependent var25.28519Adjusted R-squared0.038734S.D. dependent var201.1252S.E. of regression197.1916Akaike info criterion13.50553Sum squared resid661036.6Schwarz criterion13.60494Log likelihood-126.3025F-s

45、tatistic1.725303Durbin-Watson stat1.776498Prob(F-statistic)0.206464e与 e(-1) 、e(-2) 做回归得：Dependent Variable: EMethod: Least SquaresDate: 12/04/11 Time: 15:24Sample (adjusted): 1992 2009Included observations: 18 after adjustmentsVariableCoefficientStd. Error t-StatisticProb.C7.44976046.20912 0.1612180

46、.8741E(-1)0.4195640.244475 1.7161870.1067E(-2)-0.3798940.278641 -1.3633800.1929R-squared0.192570Mean dependent var16.45940Adjusted R-squared0.084912S.D. dependent var203.1349S.E. of regression194.3193Akaike info criterion13.52789Sum squared resid566399.7Schwarz criterion13.67629Log likelihood-118.75

47、10F-statistic1.788727Durbin-Watson stat2.055382Prob(F-statistic)0.201043由上表明不存在序列相关性。 D.W检验由异方差检验修正后的结果：Y 314.2074 0.979758 X1 1.457291X 3 22R2 0.999999 R 0.999999 F 3138195 D.W 1.365483得D.W=1.365483取 =5%，由于 n=20，k=3（包含常数项 ） ，查表得：dl =1.10 ， du=1.54由于dlDW=1.365483 du ，故: 序列相关性不确定。 拉格朗日检验Dependent Var

48、iable: EMethod: Least SquaresDate: 12/04/11 Time: 15:05Sample (adjusted): 1992 2009Included observations: 18 after adjustmentsVariableCoefficientStd. Error t-StatisticProb.Y0.0009840.002548 0.3862170.7051C-14.1479273.42247 -0.1926920.8500E(-1)0.3920090.261633 1.4983160.1563E(-2)-0.3477300.298739 -1.

49、1639920.2639R-squared0.201082Mean dependent var16.45940Adjusted R-squared0.029885S.D. dependent var203.1349S.E. of regression200.0765Akaike info criterion13.62841Sum squared resid560428.6Schwarz criterion13.82627Log likelihood-118.6557F-statistic1.174565Durbin-Watson stat2.010385Prob(F-statistic)0.3

50、546792LM n* R2 20 0.201082 4.02164取 =5%， 2 分布的临界值 20.05 (3) 7.815LM 20.05 (3)故 : 存在序列相关。 修正为了更好的提高模型的精度，我们用广义差分法对模型进行修正首先用杜宾（ durbin ）两步法估计Dependent Variable: YMethod: Least SquaresDate: 12/04/11Time: 16:18Sample (adjusted): 1992 2009Included observations: 18 after adjustmentsVariableCoefficientStd. Errort-StatisticProb.C-36.8579081.18933-0.4539750.6606Y(-1)0.7306100.3453042.1158470.0635Y(-2)0.3581040.3645190.9824020.3516X11.0973550.03037736.124880.0000X1(-1)-0.8724