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我用Eviews做的面板VEC模型, 分析结果如下。请问怎么才能看到各个系数的相伴概率(p值)?
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Vector Error Correction Estimates
Date: 04/05/15 Time: 10:32
Sample (adjusted): 2003Q4 2014Q4
Included observations: 135 after adjustments
Standard errors in ( ) & t-statistics in [ ]
Cointegrating Eq: CointEq1
DLNRF(-1) 1.000000
DLNBS(-1) -0.899205
(0.06006)
[-14.9706]
DLNCD(-1) 0.513307
(0.11946)
[ 4.29684]
DLNE(-1) -9.480812
(1.48931)
[-6.36592]
DLNER(-1) -0.029541
(0.05552)
[-0.53207]
DLNMC(-1) -6.923597
(2.19379)
[-3.15600]
C 0.040331
Error Correction: D(DLNRF) D(DLNBS) D(DLNCD) D(DLNE) D(DLNER) D(DLNMC)
CointEq1 0.028566 1.722224 -0.050546 0.003366 0.078996 0.032290
(0.00894) (0.10914) (0.06022) (0.00670) (0.18137) (0.00548)
[ 3.19610] [ 15.7793] [-0.83929] [ 0.50204] [ 0.43556] [ 5.89377]
D(DLNRF(-1)) -0.361521 -2.547860 0.078027 -0.059794 1.915477 0.035227
(0.12544) (1.53188) (0.84528) (0.09410) (2.54556) (0.07690)
[-2.88194] [-1.66322] [ 0.09231] [-0.63543] [ 0.75248] [ 0.45812]
D(DLNBS(-1)) 0.019236 0.106635 -0.102591 0.011138 0.075317 0.018360
(0.00474) (0.05792) (0.03196) (0.00356) (0.09625) (0.00291)
[ 4.05565] [ 1.84104] [-3.20997] [ 3.13032] [ 0.78253] [ 6.31466]
D(DLNCD(-1)) -0.036759 0.138103 -0.038644 -0.014350 0.095130 -0.026463
(0.01120) (0.13677) (0.07547) (0.00840) (0.22728) (0.00687)
[-3.28202] [ 1.00972] [-0.51204] [-1.70801] [ 0.41856] [-3.85441]
D(DLNE(-1)) 0.113044 10.72969 0.799673 -0.666770 -2.116437 0.190903
(0.09456) (1.15470) (0.63715) (0.07093) (1.91878) (0.05796)
[ 1.19552] [ 9.29223] [ 1.25508] [-9.40040] [-1.10301] [ 3.29358]
D(DLNER(-1)) -0.006931 -0.067185 -0.067932 0.003577 -0.427177 -0.001646
(0.00448) (0.05467) (0.03017) (0.00336) (0.09085) (0.00274)
[-1.54804] [-1.22889] [-2.25185] [ 1.06503] [-4.70204] [-0.59973]
D(DLNMC(-1)) -0.377769 15.16278 6.128421 0.384149 -5.623229 -0.616839
(0.18269) (2.23097) (1.23102) (0.13704) (3.70724) (0.11199)
[-2.06780] [ 6.79651] [ 4.97832] [ 2.80314] [-1.51682] [-5.50810]
C 0.000303 0.020574 -0.000797 0.000154 0.007006 0.000366
(0.00142) (0.01731) (0.00955) (0.00106) (0.02877) (0.00087)
[ 0.21408] [ 1.18850] [-0.08343] [ 0.14462] [ 0.24357] [ 0.42131]
R-squared 0.361822 0.797403 0.489104 0.632635 0.204649 0.511016
Adj. R-squared 0.326646 0.786236 0.460944 0.612386 0.160811 0.484064
Sum sq. resids 0.034421 5.133084 1.562876 0.019369 14.17407 0.012934
S.E. equation 0.016463 0.201042 0.110933 0.012350 0.334076 0.010092
F-statistic 10.28627 71.40862 17.36895 31.24359 4.668275 18.96031
Log likelihood 366.9624 29.13914 109.4087 405.7755 -39.42109 433.0322
Akaike AIC -5.317962 -0.313173 -1.502352 -5.892970 0.702535 -6.296774
Schwarz SC -5.145797 -0.141008 -1.330187 -5.720806 0.874699 -6.124609
Mean dependent 0.000186 0.019427 0.000284 0.000118 0.007704 0.000206
S.D. dependent 0.020063 0.434831 0.151093 0.019836 0.364683 0.014050
Determinant resid covariance (dof adj.) 4.79E-17
Determinant resid covariance 3.32E-17
Log likelihood 1411.838
Akaike information criterion -20.11612
Schwarz criterion -18.95401