1、137.6312.2031.3019.3938.6275.63146379.53320.801988147.867.6637.6023.7145.9069.25143625.87508.901989196.769.4239.9026.2149.2362.75146553.93790.501990220.539.9826.4149.9364.66148362.27844.501991223.2510.2640.3026.9450.9263.09149585.80963.201992233.1910.0541.5027.4651.5361.51149007.101106.901993265.679
2、.4943.6027.9951.8660.07147740.701244.901994335.169.2045.7028.5152.1258.22148240.601473.901995411.298.4347.8029.0452.4158.43149879.301655.701996460.688.8249.5030.4853.2360.57152380.601812.701997477.968.3050.1031.9154.9358.23153969.201980.101998474.0210.6950.2033.3555.8458.03155705.702042.201999466.80
3、8.2349.9034.7857.1657.53156372.812173.452000466.167.7550.0036.2259.3355.68156299.852421.302001469.807.7137.6660.6255.24155707.862610.782002468.957.1739.0962.0254.51154635.512993.402003476.247.1250.9040.5363.7250.08152414.963432.922004499.399.6753.1041.7665.6450.05153552.553933.032005521.207.2255.204
4、2.9967.5949.72155487.734375.70资料来源中国统计年鉴2006。(二)、计量经济学模型建立我们设定模型为下面所示的形式:利用Eviews软件进行最小二乘估计,估计结果如下表所示:Dependent Variable: YMethod: Least SquaresSample: 1986 2004Included observations: 19VariableCoefficientStd. Errort-StatisticProb. C-1102.373375.8283-2.9331840.0136X1-6.6353933.781349-1.7547690.1071X
5、318.229422.0666178.8208990.0000X42.4300398.3703370.2903160.7770X5-16.237375.894109-2.7548470.0187X6-2.1552082.770834-0.7778190.4531X70.0099620.0023284.2788100.0013X80.0633890.0212762.9793480.0125R-squared0.995823 Mean dependent var345.5232Adjusted R-squared0.993165 S.D. dependent var139.7117S.E. of
6、regression11.55028 Akaike info criterion8.026857Sum squared resid1467.498 Schwarz criterion8.424516Log likelihood-68.25514 F-statistic374.6600Durbin-Watson stat1.993270 Prob(F-statistic)0.000000表1 最小二乘估计结果回归分析报告为:二、计量经济学检验(一)、多重共线性的检验及修正、检验多重共线性(a)、直观法从“表1 最小二乘估计结果”中可以看出,虽然模型的整体拟合的很好,但是x4 x6的t统计量并不显
7、著,所以可能存在多重共线性。(b)、相关系数矩阵X2 1.000000-0.717662-0.695257-0.731326 0.737028-0.332435-0.594699 0.922286 0.935992-0.945701 0.742251 0.883804 0.986050-0.937751 0.753928 0.974675-0.974750 0.687439 0.940436-0.603539-0.887428 0.742781表2 相关系数矩阵从“表2 相关系数矩阵”中可以看出,个个解释变量之间的相关程度较高,所以应该存在多重共线性。、多重共线性的修正逐步迭代法A、一元回归8
8、20.3133151.87125.401374-51.3783616.18923-3.1736140.00560.3720410.335102113.922712.40822220632.412.50763-115.878110.071830.6444000.005554表3 y对x2的回归结果-525.889164.11333-8.20249219.460311.41604313.742740.9174210.91256341.3123610.3795029014.0910.47892-96.60526188.86280.598139表4 y对x3的回归结果-223.190569.92322
9、-3.1919370.005318.650862.2422408.3179560.8027580.79115563.8476011.2501869300.7711.34959-104.876769.188390.282182表5 y对x4的回归结果-494.1440118.1449-4.1825260.000615.779782.1987117.1768320.7518500.73725371.6146311.4797887187.1411.57919-107.057951.506910.3189590.000002表6 y对x5的回归结果1288.009143.80888.956395-15
10、.523982.351180-6.6026350.7194480.70294576.1467411.6025098571.5411.70192-108.223843.594790.3958930.000004表7 y对x6的回归结果-4417.766681.1678-6.4855770.0315280.0045076.9949430.7421480.72698073.0011911.5181390595.9611.61754-107.422248.929230.572651表8 y对x7的回归结果140.162528.966164.8388350.00020.1198270.0145438.2
11、395030.7997390.78795964.3342411.2653670361.2111.36478-105.020967.889410.203711表9 y对x8的回归结果综合比较表39的回归结果,发现加入x3的回归结果最好。以x3为基础顺次加入其他解释变量,进行二元回归,具体的回归结果如下表1015所示:-754.4481149.1701-5.0576370.000121.788651.93268911.2737513.450708.0127451.6786630.11260.9297870.92101039.2661910.3225424669.3410.47167-95.0641
12、7105.93850.595954表10 加入x2的回归结果-508.678175.73220-6.71680217.882003.7521214.7658371.7533513.8443050.4560900.65450.9184810.90829142.3096510.4718528641.7110.62097-96.4825490.136130.596359表11 加入x4的回归结果-498.155067.21844-7.41098623.975163.9671836.043370-4.3205663.553466-1.2158740.24170.9244050.91495640.7431210.3963926560.0210.54551-95.7657097.827720.607882表12 加入x5的回归结果
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