EVIEWS操作各种模型学习.docx
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EVIEWS操作各种模型学习
章、图形基础与回归
原始数据
UR
URIR
UR
URIR
1978
0.15
2.97
1997
0.31
2.22
1979
0.16
2.32
1998
0.32
2.22
1980
0.17
2.43
1999
0.34
2.35
1981
0.17
2.09
2000
0.40
2.43
1982
0.18
1.68
2001
0.41
2.48
1983
0.18
1.70
2002
0.41
2.77
1984
0.18
1.50
2003
0.42
2.85
1985
0.23
1.67
2004
0.43
2.77
1986
0.24
1.91
2005
0.43
2.83
1987
0.25
2.06
2006
0.44
2.86
1988
0.27
2.26
2007
0.44
2.87
1989
0.28
2.20
2008
0.45
2.82
1990
0.29
2.12
2009
0.46
2.85
1991
0.26
2.54
2010
0.50
2.89
1992
0.29
2.76
2011
0.52
2.84
1993
0.31
3.11
2012
0.54
2.91
1994
0.28
2.85
2013
0.55
2.91
1995
0.31
3.48
2014
0.55
2.83
1996
0.34
2.33
UR
-raESZ一os①三ueno
76543210
-PULIONjoSQsueno
URIR
:
、分布图:
JB〉3判断为正太分布
S是偏度
K是峰度
5-t
Series:
UR
Sample137
4-
Observations37
3-
2-
MeanMedianMaximumMinimumStd.Dev.
SkewnessKurtosis
0.336145
0.312400
0.547000
0.15087-
0.120626
0.143256
1.887565
Jarque-Bera2.034384
Probability0.361609
0.2
0.3
0.4
0.5
三、UR的单因素联表
TabulationofUR
Date:
09/05/15Time:
21:
25
Sample:
137
Ineludedobservations:
37
Numberofcategories:
5
Value
Count
Percent
Cumulative
Count
Cumulative
Percent
[0.1,0.2)
7
18.92
7
18.92
[0.2,0.3)
9
24.32
16
43.24
[0.3,0.4)
6
16.22
22
59.46
[0.4,0.5)
11
29.73
33
89.19
[0.5,0.6)
4
10.81
37
100.00
Total
37
100.00
37
100.00
四、协方差与相矢丫生
CovarianeeAnalysis:
Ordinary
Date:
09/05/15Time:
21:
40
Sample:
137
Ineludedobservations:
37
Covarianee
Correlation
UR
URIR
UR
0.014157
1.000000
URIR
0.033170
0.204852
0.615934
1.000000
Date:
09/05/15Time:
21:
44
Sample:
137
Ineludedobservations:
37
Correlationsareasymptoticallyconsistentapproximations
URrURIR(-i)
UR,URIR(+i>
Oi
Oi□i
匚匸匚匚匸匚匚
lag
lead
0
0.6159
0.&159
1
0.5767
0.6549
2
0.4997
0.6515
3
0.4282
0.6384
4
0.3795
0.5755
5
0.3621
0.4990
6
0.3267
0.4159
7
0.3292
0.2918
8
0.2983
0.1732
9
0.2585
0.0795
10
0.1997
0.0124
11
0.1420
-0.0556
12
□0809
-0.0998
13
0.0097
-0.1538
14
-0.0231
■0.1740
15
-0.033B
-0.1790
16
-0.0565
*0.1401
17
-0.0433
-0.1238
18
-0.0316
-0.0532
19
-0.0395
-0.0939
20
-0.1629
-0.1381
1.0
0.8
0.6
0.4
0.2
0.0
1.0
0.8
0.6
0.4
0.2
0.0
五、CDF经验分布图
URIR
UR
图
QQ-亠'八
UR
QuantilesofUR
URIR
6
I
.2
■
.3
I
.4
I.5
.6
QuantilesofURIR
84
22
o
2
6
2
6
3
4
2
2
3o
2
1—
1-
七、回归散点图
UR
邻近拟合散点图:
(分布回归的结
果)
3.6
$O
八、实际值、拟合值、残差值折线图
九、回归模型预测
URF—±2S.E
Forecast:
URF
Actual:
UR
Forecastsample:
137
Ineludedobservations:
37
RootMeanSquaredError0.093736
MeanAbsoluteError0.072711
MeanAbs.PercentError25.93140
TheilInequalityCoefficientO.133790
BiasProportion0.000000
VarianeeProportion0.237674
CovarianeeProportion0.762326
十、两回归系数的联合检验置信区间是一个椭圆区域
!
八一、Wald系数约束条件检验
WaldTest:
Equation:
Untitled
RestrictionsarelinearincgeAI匚ients
TestStatistic
Value
df
Probability
F-statisticChi-square
272.1503
(1.35)
0.0000
272.1503
1
0.0000
NullHypothesisSummary:
NormalizedRestriction[=Q)
Value
Std.Err,
-1+C
(1)+C
(2)
-0,907527
0.055012
Chow分割点检验结果
ChowBreakpointTest1991
NullHypothesis:
Nobreaksatspecifiedbreakpoints
Varyingregressors:
AllequationvariablesEquationSample:
19782014
F'Statistic
11.00551
Prob.F(2r33)
0.0002
Loglikelihoodratio
18.90797
ProbJChi-Square
(2)
0.0001
WaldStatistic
22.01103
Prob.Chi-Squa(e
(2)
0.0000
F、LR的P值显著,表示:
模型无显著的结构变化
十二、Chow稳定性检验(p75)
Chow预测结果:
ChowForecastTest:
Forecastfrom1991to2014
F-statistic
Loglikelihoodratio
4.744782
89.83840
PfOb.F(24,11)
Prob.Chi-Square(24)
0.0050
0.0000
TestEquation:
DependentVariable:
URMethod:
LeastSquares
Date:
09/07/15Time:
10:
31
Sample:
197S1990
Ineluded!
observations:
13
Coefficient
Std.Errort-Statistic
Prob.
URIR
C
-0014716
0.241289
0.037436・0393094
0.0787753.063016
07018
0.0100
R-squared
AdjustedR-squared
S.Eofregression
SumsquaredresidLoglikelihoodF-statistic
Prob(F-statistic)
0.013853
*0.075797
0.051024
0.028637
21.32073
0.154523
0.701762
Lieandependentvar
S.Ddependentvar.Akaikeinfocriterion
SchwarzcriterionHannan-Ouinncriter.
Durbin-Watsonstat
0.210827
0.0491932972421・2.885505・
2990286
0.127458
十三、零均值附近的递归残差曲线图
★注:
红线为5%的临界值线,在1991年后的CUSUM曲线变得十分陡哨,说明:
回归方程系数并不是稳定的。
One-Step
Probability
Recursiv
eResiduals
3.—步预测检验:
4.N步预测检验:
进行一系列的Chow检验
N-StepProbability
RecursiveResiduals
★注:
上部分是递归残差,下部分是检验显著性的概率
.3
.2
十四、White异方差检验
Obs*R-squared=10.4,其P值=0.0055表示残差存在异方差性。
F统计量表示:
检验辅助方程的整体显著性,下图中整体显著。
HeteroskedasticityTest:
White
TestEquation:
DependentVariaDle:
RESIDA2Method:
LeastSquaresDate:
09/07/15Time:
11:
02
Sample:
19782014
Irt匚ludedobservations:
37
CoefficientStd.Errort-StatisticProb.
c
URIR
URIRA2
0.039819
-0.042859
0.011780
0.0458S60.867779
0038582-1110863
0.0079411.483508
0.3916
0.2744
0.1472
R-squared
0.281447
Meandependentvar
0.008786
AdjustedR-squared
0239179
S.D.dependentvar
0013287
S.Eofregression
0.011590
Akaikeinfocriterion
-5999002
Sumsquaredresid
0.004567
£匚hwamcriterion
-5.869187
Loglikelihood
1139963
Hannar/Quinnenter.
-5953754
F-statistic
6.658663
Durbin-Watsonstat
0.374222
Prob(F-statistic)
0.003629
十五、WLS加权最小二乘法
DependentVariable:
UR
Method:
LeastSquaresDate:
09/07715Time:
11:
29
Sample:
19782014includedobservations:
37
Weightingseries:
W
Coefficient
Std.Error
t-StatisticProb.
URIR
0.168557
0.031139
5,4130330.0000
C
-0.086069
0.076542
-1.1244650.2685
WeightedStatistics
R-squared
0.455684
(Jeandependentvar
0.328603
AdjustedR-squared
0440132
S.Ddependentvar
0.100794
SE,ofregression
0.090040
Akaikeinlacriterion
*1.924588
Sumsquaredresid
0.283752
Schwarzcriterion
-1,837512
LogliKelitiaod
37.50488
HannarpQuinncriter
-1893890
F-statistic
29.30092
Durbin-Watsonstat
0.279531
Prob(F-statistic)0.000005
UnweightedStatisti匚s
R-squared
□378738
Meandependentvar
0.33614S
AdjustedR-squared
0.350987
SD.dependentvar
0.120526
S.E.ofregression
0.096427
Sumsquaredresid
0.325433
Durbin-Watsonstat
0.310271
R-SQuarecl
AdjustedR-squarecdlS.E.ofreoresslion
SLimscjuoreclresidlLoo1
11h(DF-statistic
Prob(F-statistic>
O„61S958OS810450.061509
0.12V852
S2.79260
MeandependentvarS.DdependentvarAKalKeinTocriterionSchwar-z・criterion
MAninAn-Ouinn:
crirtierDurbin-Watson
O0-950292.637-453・2„4I333OO-2.S76OS6
64271O.DDOOO1
1,,204229
十六、残差自相尖图及其Q检验统计量
CorrelogramofResiduals
Date:
09/07/15Time:
15:
50
Sample:
19782014
Ineludedobservations:
37
1-16阶的p值都小于0.01,说明拒绝原假设,残差序歹U存在自相尖性。
十七、残差自相矢LM检验结果
Breuscn-GocrrreyserialCorrslatJonLMT©st:
F与Obs两个的P值显示:
存在自相矢
十八、Newey-West—致协方差估计
Dependentvariable:
UR
Method:
LeastSquares
Date:
09/07/15Time:
1635
Sample:
19782014
Includedobservations:
37
NeweyAVestHACStandardErrors&Covarianee(lagtruncation=3)
Coefficient
StdLErrort-Statistic
Prob.
URIR
0.161922
0.0508133,186620
0.0030
C
-0.069449
0.109907-0.631837
0.5316
R-squared
0.379375
Meandependentvar
0.336145
AdjustedR-squared
0.361643
S.Ddependentvar
0.120626
S.E.ofregression
0.096377
Akaikeinfocriterion
-1.788557
Sumsquaredresid
0.325099
Schwarzcriterion
-1,701481
Loglikelihood
35.08831
Hannan・Quinncriter.
*1.757959
F-statistic
2139474
Durbin-Watsonstat
0.289676
Prob(F-statiStic)
0.000049
十九、两阶段TSLS估计检验结果
DependentVariable:
URPethod:
Two-StageLeastSquaresDate:
09/07/15Time:
16:
43
Sample:
19782014
Indudedobservations:
37
InstrumentlistCUR
Coefficient
Std.Errort-Statistic
Prob.
URIR
0.426813
0.0922754.625445
0.0000
C
*0732965
0.232564-3.151675
0.0033
R-squared
-0.635916
Meandependentvar
0.336145
AdjustedR-squared
-0.682657
S.D.dependentvar
0.120626
S.E,ofregression
0.156473
Sumsquaredredid
0856934
F-statistic
2139474
Durbin-Watsonstat
0.632913
Prob(F-statistic)
0.000049
SecontTStageSSR
4.14E*30
二十、广义矩估计GMM检验结果
toependentVariable:
UR
Method:
GeneralizedMethodofMoments
Date:
09/07/15Time:
16:
51
Sample:
19782014
Ineludedobservations:
37
Kernel:
Bartlett,Bandwidth:
Fixed(3).NoprewhiteningSimultaneousweightingmatrix&coefficientiterationConvergeneeachievedafter:
1weightmatrix,2totalcoefiterationsInstrumentlistCURIR
Coefficient
Std.Error1-Statistic
Prob.
URIR
0.161922
0.0509333.179114
0.0031
C
-0.069449
0.112195-0.619000
0.5399
R-squared
0379375
Meandependent回
0.336145
AdjustedR-squared
0.361643
S.D.dependentvar
0.120626
S・E.ofregression
0.096377
Sumsquaredresid
0.325099
Durbin-Watsonstat
0.289676
J-statistic
1.58E-27
章、离散及受限制因变量模型
、原始数据
obs
GPA
SE
PSI
Grade
1
2.66
20
0
0
2
2.89
22
0
0
3
3.28
24
0
0
4
2.29
12
0
0
5
4
21
0
1
6
2.86
17
0
0
7
2.76
17
0
0
8
2.87
21
0
0
9
3.03
25
0
0
10
3.92
29
0
1
11
2.63
20
0
0
12
3.32
23
0
0
13
3.57
23
0
0
14
3.26
25
0
1
15
3.53
26
0
0
16
2.74
19
0
0
17
2.75
25
0
0
18
2.83
19
0
0
19
3.12
23
1
0
20
3.16
25
1
1
21
2.06
22
1
0
22
3.62
28
1
1
23
2.89
14
1
0
24
3.51
26
1
0
25
3.54
24
1
1
26
2.83
27
1
1
27
3.39
17
1
1
28
2.67
24
1
0
29
3.65
21
1
1
30
4
23
1
1
31
3.1
21
1
0
32
2.39
19
1
1
1、Logit模