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计量经济学论文范文eviews
计量经济学论文范文eviews
《我国财政收入影响因素分析》
班级:
09财政1班
姓名:
李睿
学号:
200909111023
指导教师:
单海鹏
完成时间:
2011年12月4日
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。
一、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X2
其他收入X3
就业人口总数X4
1990
2937.1
2821.86
18667.8
299.53
64749
1991
3149.48
2990.17
21781.5
240.1
65491
1992
3483.37
3296.91
26923.5
265.15
66152
1993
4348.95
4255.3
35333.9
191.04
66808
1994
5218.1
5126.88
48197.9
280.18
67455
1995
6242.2
6038.04
60793.7
396.19
68065
1996
7407.99
6909.82
71176.6
724.66
68950
1997
8651.14
8234.04
78973
682.3
69820
1998
9875.95
9262.8
84402.3
833.3
70637
1999
11444.08
10682.58
89677.1
925.43
71394
2000
13395.23
12581.51
99214.6
944.98
72085
2001
16386.04
15301.38
109655.2
1218.1
73025
2002
18903.64
17636.45
120332.7
1328.74
73740
2003
21715.25
20017.31
135822.8
1691.93
74432
2004
26396.47
24165.68
159878.3
2148.32
75200
2005
31649.29
28778.54
184937.4
2707.83
75825
2006
38760.2
34804.35
216314.4
3683.85
76400
2007
51321.78
45621.97
265810.3
4457.96
76990
2008
61330.35
54223.79
314045.4
5552.46
77480
2009
68518.3
59521.59
340506.9
7215.72
77995
二、模型建立
1、散点图分析
2、单因素或多变量间关系分析
Y
X1
X2
X3
X4
Y
1
0.998913461147853
0.993479045290804
0.877014488679564
0.983602719841508
X1
0.998913461147853
1
0.993740267718469
0.855637734744782
0.984935296593492
X2
0.993479045290804
0.993740267718469
1
0.856183580228471
0.986241165680459
X3
0.877014488679564
0.855637734744782
0.856183580228471
1
0.810940334650381
X4
0.983602719841508
0.984935296593492
0.986241165680459
0.810940334650381
1
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:
DependentVariable:
Y
Method:
LeastSquares
Date:
11/14/11Time:
17:
51
Sample:
19902009
Includedobservations:
20
Variable
Coefficient
Std.Error
t-Statistic
Prob.
C
7299.523
1691.814
4.314614
0.0006
X1
1.062802
0.021108
50.34972
0.0000
X2
0.001770
0.004528
0.391007
0.7013
X3
0.873369
0.119806
7.289852
0.0000
X4
-0.115975
0.026580
-4.363160
0.0006
R-squared
0.999978
Meandependentvar
20556.75
AdjustedR-squared
0.999972
S.D.dependentvar
19987.03
S.E.ofregression
106.6264
Akaikeinfocriterion
12.38886
Sumsquaredresid
170537.9
Schwarzcriterion
12.63779
Loglikelihood
-118.8886
F-statistic
166897.9
Durbin-Watsonstat
1.496517
Prob(F-statistic)
0.000000
(4.314614)(50.34972)(0.391007)(7.289852)(-4.363160)
三、模型检验
1.计量经济学意义检验
⑴多重共线性检验与解决
求相关系数矩阵,得到:
CorrelationMatrix
Y
X1
X2
X3
X4
1
0.998913461147853
0.993479045290804
0.877014488679564
0.983602719841508
0.998913461147853
1
0.993740267718469
0.855637734744782
0.984935296593492
0.993479045290804
0.993740267718469
1
0.856183580228471
0.986241165680459
0.877014488679564
0.855637734744782
0.856183580228471
1
0.810940334650381
0.983602719841508
0.984935296593492
0.986241165680459
0.810940334650381
1
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:
①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:
DependentVariable:
Y
Method:
LeastSquares
Date:
11/22/11Time:
23:
02
Sample:
19902009
Includedobservations:
20
Variable
Coefficient
Std.Error
t-Statistic
Prob.
C
-755.6610
145.2330
-5.203094
0.0001
X1
1.144994
0.005760
198.7931
0.0000
R-squared
0.999545
Meandependentvar
20556.75
AdjustedR-squared
0.999519
S.D.dependentvar
19987.03
S.E.ofregression
438.1521
Akaikeinfocriterion
15.09765
Sumsquaredresid
3455590.
Schwarzcriterion
15.19722
Loglikelihood
-148.9765
F-statistic
39518.70
Durbin-Watsonstat
0.475046
Prob(F-statistic)
0.000000
作Y与X2的回归,结果如下:
DependentVariable:
Y
Method:
LeastSquares
Date:
11/22/11Time:
23:
06
Sample:
19902009
Includedobservations:
20
Variable
Coefficient
Std.Error
t-Statistic
Prob.
C
-5222.077
861.2067
-6.063674
0.0000
X2
0.207689
0.005548
37.43267
0.0000
R-squared
0.987317
Meandependentvar
20556.75
AdjustedR-squared
0.986612
S.D.dependentvar
19987.03
S.E.ofregression
2312.610
Akaikeinfocriterion
18.42478
Sumsquaredresid
96267005
Schwarzcriterion
18.52435
Loglikelihood
-182.2478
F-statistic
1401.205
Durbin-Watsonstat
0.188013
Prob(F-statistic)
0.000000
作Y与X3的回归,结果如下:
DependentVariable:
Y
Method:
LeastSquares
Date:
11/22/11Time:
23:
08
Sample:
19902009
Includedobservations:
20
Variable
Coefficient
Std.Error
t-Statistic
Prob.
C
2607.879
773.9988
3.369358
0.0034
X3
10.03073
0.294311
34.08209
0.0000
R-squared
0.984740
Meandependentvar
20556.75
AdjustedR-squared
0.983893
S.D.dependentvar
19987.03
S.E.ofregression
2536.645
Akaikeinfocriterion
18.60971
Sumsquaredresid
1.16E+08
Schwarzcriterion
18.70929
Loglikelihood
-184.0971
F-statistic
1161.589
Durbin-Watsonstat
1.194389
Prob(F-statistic)
0.000000
作Y与X4的回归,结果如下:
DependentVariable:
Y
Method:
LeastSquares
Date:
11/22/11Time:
23:
08
Sample:
19902009
Includedobservations:
20
Variable
Coefficient
Std.Error
t-Statistic
Prob.
C
-272959.3
37203.65
-7.336894
0.0000
X4
4.097403
0.518467
7.902918
0.0000
R-squared
0.776276
Meandependentvar
20556.75
AdjustedR-squared
0.763846
S.D.dependentvar
19987.03
S.E.ofregression
9712.824
Akaikeinfocriterion
21.29492
Sumsquaredresid
1.70E+09
Schwarzcriterion
21.39449
Loglikelihood
-210.9492
F-statistic
62.45611
Durbin-Watsonstat
0.157356
Prob(F-statistic)
0.000000
②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
DependentVariable:
Y
Method:
LeastSquares
Date:
11/22/11Time:
23:
09
Sample:
19902009
Includedobservations:
20
Variable
Coefficient
Std.Error
t-Statistic
Prob.
C
-188.4285
239.0743
-0.788159
0.4415
X1
1.281594
0.049472
25.90568
0.0000
X2
-0.025055
0.009029
-2.774908
0.0130
R-squared
0.999687
Meandependentvar
20556.75
AdjustedR-squared
0.999650
S.D.dependentvar
19987.03
S.E.ofregression
374.0345
Akaikeinfocriterion
14.82405
Sumsquaredresid
2378330.
Schwarzcriterion
14.97341
Loglikelihood
-145.2405
F-statistic
27118.20
Durbin-Watsonstat
0.683510
Prob(F-statistic)
0.000000
作Y与X1、X3的回归,结果如下
DependentVariable:
Y
Method:
LeastSquares
Date:
11/22/11Time:
23:
10
Sample:
19902009
Includedobservations:
20
Variable
Coefficient
Std.Error
t-Statistic
Prob.
C
-351.1054
83.15053
-4.222527
0.0006
X1
0.992813
0.018707
53.07196
0.0000
X3
1.356936
0.165109
8.218410
0.0000
R-squared
0.999908
Meandependentvar
20556.75
AdjustedR-squared
0.999898
S.D.dependentvar
19987.03
S.E.ofregression
202.1735
Akaikeinfocriterion
13.59361
Sumsquaredresid
694859.9
Schwarzcriterion
13.74297
Loglikelihood
-132.9361
F-statistic
92839.33
Durbin-Watsonstat
1.177765
Prob(F-statistic)
0.000000
作Y与X1、X4的回归,结果如下
DependentVariable:
Y
Method:
LeastSquares
Date:
11/22/11Time:
23:
10
Sample:
19902009
Includedobservations:
20
Variable
Coefficient
Std.Error
t-Statistic
Prob.
C
11853.46
1824.522
6.496748
0.0000
X1
1.185886
0.006645
178.4608
0.0000
X4
-0.186645
0.026984
-6.917003
0.0000
R-squared
0.999881
Meandependentvar
20556.75
AdjustedR-squared
0.999867
S.D.dependentvar
19987.03
S.E.ofregression
230.8464
Akaikeinfocriterion
13.85886
Sumsquaredresid
905931.0
Schwarzcriterion
14.00822
Loglikelihood
-135.5886
F-statistic
71206.90
Durbin-Watsonstat
1.459938
Prob(F-statistic)
0.000000
③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
DependentVariable:
Y
Method:
LeastSquares
Date:
11/22/11Time:
23:
13
Sample:
19902009
Includedobservations:
20
Variable
Coefficient
Std.Error
t-Statistic
Prob.
C
-76.04458
100.1724
-0.759137
0.4588
X1
1.085924
0.029801
36.43881
0.0000
X3
1.210853
0.133444
9.073877
0.0000
X2
-0.014073
0.003944
-3.567901
0.0026
R-squared
0.999949
Meandependentvar
20556.75
AdjustedR-squared
0.999939
S.D.dependentvar
19987.03
S.E.ofregression
155.5183
Akaikeinfocriterion
13.10826
Sumsquaredresid
386975.0
Schwarzcriterion
13.30741
Loglikelihood
-127.0826
F-statistic
104602.9
Durbin-Watsonstat
1.196933
Prob(F-statistic)
0.000000
作Y与X1、X3、X4的回归,结果如下
DependentVariable:
Y
Method:
LeastSquare