1、2990.1721781.5240.16549119923483.373296.9126923.5265.156615219934348.954255.335333.9191.046680819945218.15126.8848197.9280.186745519956242.26038.0460793.7396.196806519967407.996909.8271176.6724.666895019978651.148234.0478973682.36982019989875.959262.884402.3833.370637199911444.0810682.5889677.1925.4
2、371394200013395.2312581.5199214.6944.9872085200116386.0415301.38109655.21218.173025200218903.6417636.45120332.71328.7473740200321715.2520017.31135822.81691.9374432200426396.4724165.68159878.32148.3275200200531649.2928778.54184937.42707.8375825200638760.234804.35216314.43683.8576400200751321.7845621.
3、97265810.34457.9676990200861330.3554223.79314045.45552.4677480200968518.359521.59340506.97215.7277995三、 模型建立1、 散点图分析2、 单因素或多变量间关系分析YX1X2X3X410.9989134611478530.9934790452908040.8770144886795640.9836027198415080.9937402677184690.8556377347447820.9849352965934920.8561835802284710.9862411656804590.8109
4、40334650381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：3、 模型预模拟由eviews做ols回归得到结果：Dependent Variable: YMethod: Least SquaresDate: 11/14/11 Time: 17:51Sample: 1990 2009Included observations: 20VariableCoefficientStd. Errort-StatisticProb.C7299.5231691.8144.3146140.
5、00061.0628020.02110850.349720.00000.0017700.0045280.3910070.70130.8733690.1198067.289852-0.1159750.026580-4.36316R-squared0.999978Mean dependent var20556.75Adjusted R-squared0.999972S.D. dependent var19987.03S.E. of regression106.6264Akaike info criterion12.38886Sum squared resid170537.9Schwarz crit
6、erion12.63779Log likelihood-118.8886F-statistic166897.9Durbin-Watson stat1.496517Prob（F-statistic）0.000000 （4.314614） （ 50.34972 ） （ 0.391007） （ 7.289852） （ -4.363160） 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 Matrix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: 11/22/11
7、 Time: 23:02-755.6610145.2330-5.2030940.00011.1449940.005760198.79310.9995450.999519438.152115.097653455590.15.19722-148.976539518.700.475046作Y与X2的回归，结果如下:06-5222.077861.2067-6.0636740.2076890.00554837.432670.9873170.9866122312.61018.424789626700518.52435-182.24781401.2050.188013作Y与X3的回归，结果如下:082607
8、.879773.99883.3693580.003410.030730.29431134.082090.9847400.9838932536.64518.609711.16E+0818.70929-184.09711161.5891.194389作Y与X4的回归，结果如下:-272959.337203.65-7.3368944.0974030.5184677.9029180.7762760.7638469712.82421.294921.70E+0921.39449-210.949262.456110.157356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
9、分别代入回归得:作Y与X1、X2的回归，结果如下09-188.4285239.0743-0.7881590.44151.2815940.04947225.90568-0.0250550.009029-2.7749080.01300.9996870.999650374.034514.824052378330.14.97341-145.240527118.200.683510作Y与X1、X3的回归，结果如下10-351.105483.15053-4.2225270.9928130.01870753.071961.3569360.1651098.2184100.9999080.999898202.1
10、73513.59361694859.913.74297-132.936192839.331.177765作Y与X1、X4的回归，结果如下11853.461824.5226.4967481.1858860.006645178.4608-0.1866450.026984-6.9170030.9998810.999867230.846413.85886905931.014.00822-135.588671206.901.459938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13-76.04458100.1724-0.7591370.45881.0859240.02980136.438811.2108530.1334449.073877-0.0140730.003944-3.5679010.00260.9999490.999939
