Eviews实验报告.docx
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Eviews实验报告.docx
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Eviews实验报告
江西农业大学经济贸易学院
学生实验报告
课程名称:
计量经济学
专业班级:
经济1201班
姓名:
学号:
指导教师:
徐冬梅
职 称:
讲师
实验日期:
2014.12.11
学生实验报告
学生姓名
学号
组员:
实验项目
EVIEWS的使用
√必修□选修
√演示性实验√验证性实验□操作性实验□综合性实验
实验地点
管理模拟实验室
实验仪器台号
指导教师
实验日期及节次
一、实验目的及要求
1、目的
会使用EVIEWS对计量经济模型进行分析
2、内容及要求
(1)对经典线形回归模型进行参数估计、参数的检验与区间估计,对模型总体进行显著性检验;
(2)异方差的检验及其处理;
(3)自相关的检验及其处理;
(4)多重共线性检验及其处理;
二、仪器用具
仪器名称
规格/型号
数量
备注
计算机
1
无网络环境
Eviews
1
三、实验方法与步骤
(一)数据的输入、描述及其图形处理;
(二)方程的估计;
(三)参数的检验、违背经典假定的检验;
(四)模型的处理与预测
四、实验结果与数据处理
实验一:
中国城镇居民人均消费支出模型
数据散点图:
通过Eviews估计参数方程
回归方程:
DependentVariable:
Y
Method:
LeastSquares
Date:
11/27/14Time:
15:
02
Sample:
131
Includedobservations:
31
Variable
Coefficient
Std.Error
t-Statistic
Prob.
X
1.359477
0.043302
31.39525
0.0000
C
-57.90655
377.7595
-0.153289
0.8792
R-squared
0.971419
Meandependentvar
11363.69
AdjustedR-squared
0.970433
S.D.dependentvar
3294.469
S.E.ofregression
566.4812
Akaikeinfocriterion
15.57911
Sumsquaredresid
9306127.
Schwarzcriterion
15.67162
Loglikelihood
-239.4761
F-statistic
985.6616
Durbin-Watsonstat
1.294974
Prob(F-statistic)
0.000000
得出估计方程为:
Y=1.35947661442*X-57.9065479515
异方差检验
1、图示检验法
图形呈现离散趋势,大致判断存在异方差性。
2、Park检验
DependentVariable:
LOG(E2)
Method:
LeastSquares
Date:
11/27/14Time:
16:
16
Sample:
131
Includedobservations:
31
Variable
Coefficient
Std.Error
t-Statistic
Prob.
C
19.82562
19.85359
0.998591
0.3263
LOG(X)
-0.956403
2.204080
-0.433924
0.6676
R-squared
0.006451
Meandependentvar
11.21371
AdjustedR-squared
-0.027809
S.D.dependentvar
2.894595
S.E.ofregression
2.934568
Akaikeinfocriterion
5.053338
Sumsquaredresid
249.7389
Schwarzcriterion
5.145854
Loglikelihood
-76.32674
F-statistic
0.188290
Durbin-Watsonstat
2.456500
Prob(F-statistic)
0.667555
看到图中LOG(E2)中P值为0.6676>0.05,所以不存在异方差性
3、G-Q检验
e1检验:
DependentVariable:
X
Method:
LeastSquares
Date:
11/27/14Time:
16:
41
Sample:
112
Includedobservations:
12
Variable
Coefficient
Std.Error
t-Statistic
Prob.
C
4642.028
2014.183
2.304671
0.0439
Y
0.231046
0.215824
1.070530
0.3095
R-squared
0.102820
Meandependentvar
6796.390
AdjustedR-squared
0.013102
S.D.dependentvar
293.2762
S.E.ofregression
291.3486
Akaikeinfocriterion
14.33793
Sumsquaredresid
848840.2
Schwarzcriterion
14.41875
Loglikelihood
-84.02758
F-statistic
1.146034
Durbin-Watsonstat
0.445146
Prob(F-statistic)
0.309538
e2检验:
DependentVariable:
X
Method:
LeastSquares
Date:
11/27/14Time:
16:
42
Sample:
2031
Includedobservations:
12
Variable
Coefficient
Std.Error
t-Statistic
Prob.
C
583.4526
593.4370
0.983175
0.3487
Y
0.697748
0.040196
17.35870
0.0000
R-squared
0.967879
Meandependentvar
10586.89
AdjustedR-squared
0.964667
S.D.dependentvar
2610.864
S.E.ofregression
490.7655
Akaikeinfocriterion
15.38082
Sumsquaredresid
2408507.
Schwarzcriterion
15.46164
Loglikelihood
-90.28493
F-statistic
301.3245
Durbin-Watsonstat
2.748144
Prob(F-statistic)
0.000000
第一个图中的残差平方和为848840.2
第二个图中的残差平方和为2408507
所以F值为2408507/848840.2=2.8374<2.97,所以不存在异方差性
4、White检验
WhiteHeteroskedasticityTest:
F-statistic
2.240402
Probability
0.125152
Obs*R-squared
4.276524
Probability
0.117860
TestEquation:
DependentVariable:
RESID^2
Method:
LeastSquares
Date:
11/27/14Time:
16:
50
Sample:
131
Includedobservations:
31
Variable
Coefficient
Std.Error
t-Statistic
Prob.
C
-2135113.
1158576.
-1.842876
0.0760
X
503.7331
242.2078
2.079756
0.0468
X^2
-0.023609
0.011650
-2.026590
0.0523
R-squared
0.137952
Meandependentvar
300197.6
AdjustedR-squared
0.076378
S.D.dependentvar
347663.4
S.E.ofregression
334122.9
Akaikeinfocriterion
28.36817
Sumsquaredresid
3.13E+12
Schwarzcriterion
28.50694
Loglikelihood
-436.7067
F-statistic
2.240402
Durbin-Watsonstat
1.871252
Prob(F-statistic)
0.125152
P值为0.11786>0.05,所以不存在异方差性
通过四种不同的检验得知除了图示检验法得出异方差的结论,其他的检验的结论都是不存在异方差的。
5、WLS(加权最小二乘法)修正
DependentVariable:
Y
Method:
LeastSquares
Date:
11/27/14Time:
17:
14
Sample:
131
Includedobservations:
31
Weightingseries:
E3
Variable
Coefficient
Std.Error
t-Statistic
Prob.
C
-85.69426
24.15675
-3.547425
0.0013
X
1.362221
0.002307
590.5615
0.0000
WeightedStatistics
R-squared
1.000000
Meandependentvar
13474.53
AdjustedR-squared
1.000000
S.D.dependentvar
61353.74
S.E.ofregression
27.93264
Akaikeinfocriterion
9.559810
Sumsquaredresid
22626.73
Schwarzcriterion
9.652325
Loglikelihood
-146.1770
F-statistic
348762.9
Durbin-Watsonstat
2.061818
Prob(F-statistic)
0.000000
UnweightedStatistics
R-squared
0.971413
Meandependentvar
11363.69
AdjustedR-squared
0.970427
S.D.dependentvar
3294.469
S.E.ofregression
566.5415
Sumsquaredresid
9308110.
Durbin-Watsonstat
2.178992
实验二:
中国粮食生产函数
1、回归方程
DependentVariable:
LOG(Y)
Method:
LeastSquares
Date:
12/11/14Time:
15:
06
Sample:
19832007
Includedobservations:
25
Variable
Coefficient
Std.Error
t-Statistic
Prob.
LOG(X1)
0.381145
0.050242
7.586182
0.0000
LOG(X2)
1.222289
0.135179
9.042030
0.0000
LOG(X3)
-0.081110
0.015304
-5.300024
0.0000
LOG(X4)
-0.047229
0.044767
-1.054980
0.3047
LOG(X5)
-0.101174
0.057687
-1.753853
0.0956
C
-4.173174
1.923624
-2.169434
0.0429
R-squared
0.981597
Meandependentvar
10.70905
AdjustedR-squared
0.976753
S.D.dependentvar
0.093396
S.E.ofregression
0.014240
Akaikeinfocriterion
-5.459968
Sumsquaredresid
0.003853
Schwarzcriterion
-5.167438
Loglikelihood
74.24960
F-statistic
202.6826
Durbin-Watsonstat
1.791427
Prob(F-statistic)
0.000000
得出回归方程为:
LOG(Y)=0.381144581612*LOG(X1)+1.22228859801*LOG(X2)-0.0811098881534*LOG(X3)-0.04722870996*LOG(X4)-0.101173736285*LOG(X5)-4.173********
通过检验结果可知R2较大且接近于1,而且F=202.6826>F0.05(5,19)=2.74,故认为粮食产量与上述变量之间总体线性关系显著。
但是由于其中X4、X5前的参数估计值未通过t检验,且符号的经济意义不合理,故认为解释变量之间存在多重共线。
2、相关系数表
LNX1
LNX2
LNX3
LNX4
LNX5
LNX1
1.000000
-0.568744
0.451700
0.964357
0.440205
LNX2
-0.568744
1.000000
-0.214097
-0.697625
-0.073270
LNX3
0.451700
-0.214097
1.000000
0.398780
0.411279
LNX4
0.964357
-0.697625
0.398780
1.000000
0.279528
LNX5
0.440205
-0.073270
0.411279
0.279528
1.000000
由表可知LnX1与LnX2之间存在高度的线性相关性
3、简单的回归形式
LnY与LnX1
DependentVariable:
LNY
Method:
LeastSquares
Date:
12/11/14Time:
15:
15
Sample:
19832007
Includedobservations:
25
Variable
Coefficient
Std.Error
t-Statistic
Prob.
LNX1
0.224005
0.025515
8.779293
0.0000
C
8.902008
0.206034
43.20657
0.0000
R-squared
0.770175
Meandependentvar
10.70905
AdjustedR-squared
0.760182
S.D.dependentvar
0.093396
S.E.ofregression
0.045737
Akaikeinfocriterion
-3.255189
Sumsquaredresid
0.048114
Schwarzcriterion
-3.157679
Loglikelihood
42.68986
F-statistic
77.07599
Durbin-Watsonstat
0.939435
Prob(F-statistic)
0.000000
LnY与LnX2
DependentVariable:
LNY
Method:
LeastSquares
Date:
12/11/14Time:
15:
16
Sample:
19832007
Includedobservations:
25
Variable
Coefficient
Std.Error
t-Statistic
Prob.
LNX2
-0.383434
0.509669
-0.752321
0.4595
C
15.15748
5.912971
2.563429
0.0174
R-squared
0.024017
Meandependentvar
10.70905
AdjustedR-squared
-0.018417
S.D.dependentvar
0.093396
S.E.ofregression
0.094252
Akaikeinfocriterion
-1.809063
Sumsquaredresid
0.204321
Schwarzcriterion
-1.711553
Loglikelihood
24.61329
F-statistic
0.565986
Durbin-Watsonstat
0.335219
Prob(F-statistic)
0.459489
LnY与LnX3
DependentVariable:
LNY
Method:
LeastSquares
Date:
12/11/14Time:
15:
18
Sample:
19832007
Includedobservations:
25
Variable
Coefficient
Std.Error
t-Statistic
Prob.
LNX3
0.108067
0.085271
1.267335
0.2177
C
9.619722
0.859744
11.18905
0.0000
R-squared
0.065274
Meandependentvar
10.70905
AdjustedR-squared
0.024634
S.D.dependentvar
0.093396
S.E.ofregression
0.092239
Akaikeinfocriterion
-1.852255
Sumsquaredresid
0.195684
Schwarzcriterion
-1.754745
Loglikelihood
25.15319
F-statistic
1.606139
Durbin-Watsonstat
0.597749
Prob(F-statistic)
0.217717
LnY与LnX4
DependentVariable:
LNY
Method:
LeastSquares
Date:
12/11/14Time:
15:
18
Sample:
19832007
Includedobservations:
25
Variable
Coefficient
Std.Error
t-Statistic
Prob.
LNX4
0.166976
0.028274
5.905670
0.0000
C
8.949090
0.298255
30.00479
0.0000
R-squared
0.602605
Meandependentvar
10.70905
AdjustedR-squared
0.585327
S.D.dependentvar
0.093396
S.E.ofregression
0.060143
Akaikeinfocriterion
-2.707578
Sumsquaredresid
0.083194
Schwarzcriterion
-2.610068
Loglikelihood
35.84472
F-statistic
34.87693
Durbin-Watsonstat
0.625528
Prob(F-statistic)
0.000005
LnY与LnX5
DependentVariable:
LNY
Method:
LeastSquares
Date:
12/11/14Time:
15:
19
Sample:
19832007
Includedobservations:
25
Variable
Coefficient
Std.Er
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