如果JJ检验通过多变量方程为协整然后做向量误差修正模型时候结果怎么看~~~~

求助啊~~~~ 能帮我看看下面这个可以通过么~~
Vector Error Correction Estimates   
Date: 09/14/09   Time: 15:06   
Sample (adjusted): 1981 2007   
Included observations: 27 after adjustments   
Standard errors in ( ) & t-statistics in [ ]   
   
Cointegrating Eq:  CointEq1   
   
LOGSJGDP(-1)  1.000000   
   
LOGEC(-1) -5.903748   
  (0.47053)   
[-12.5471]   
   
LOGCAP52(-1)  0.154888   
  (0.12128)   
[ 1.27713]   
   
LOGLAB(-1)  3.510188   
  (0.37652)   
[ 9.32283]   
   
C  16.93521   
   
Error Correction: D(LOGSJGDP) D(LOGEC) D(LOGCAP52) D(LOGLAB)
   
CointEq1  0.103859  0.073134  0.145393 -0.170853
  (0.08951)  (0.03721)  (0.04948)  (0.06799)
[ 1.16028] [ 1.96558] [ 2.93868] [-2.51291]
   
D(LOGSJGDP(-1))  0.111534  0.036374 -0.190697 -0.066066
  (0.21935)  (0.09118)  (0.12124)  (0.16661)
[ 0.50848] [ 0.39893] [-1.57288] [-0.39653]
   
D(LOGSJGDP(-2)) -0.179508 -0.059983 -0.158598  0.052594
  (0.22880)  (0.09511)  (0.12647)  (0.17379)
[-0.78455] [-0.63069] [-1.25407] [ 0.30263]
   
D(LOGEC(-1))  0.741695  0.126934  0.850775 -0.370830
  (0.54919)  (0.22828)  (0.30355)  (0.41715)
[ 1.35052] [ 0.55604] [ 2.80274] [-0.88897]
   
D(LOGEC(-2))  0.998922  0.381998  0.346871 -0.258794
  (0.50147)  (0.20845)  (0.27718)  (0.38090)
[ 1.99198] [ 1.83259] [ 1.25145] [-0.67943]
   
D(LOGCAP52(-1))  0.025428  0.137863  0.839982  0.306241
  (0.37874)  (0.15743)  (0.20934)  (0.28768)
[ 0.06714] [ 0.87570] [ 4.01253] [ 1.06453]
   
D(LOGCAP52(-2)) -0.387508 -0.273539 -0.282233 -0.246105
  (0.35391)  (0.14711)  (0.19561)  (0.26881)
[-1.09495] [-1.85944] [-1.44282] [-0.91552]
   
D(LOGLAB(-1)) -0.380605 -0.014484 -0.528845  0.454688
  (0.33414)  (0.13889)  (0.18469)  (0.25380)
[-1.13906] [-0.10428] [-2.86345] [ 1.79151]
   
D(LOGLAB(-2)) -0.654481 -0.012381 -0.323104  0.624739
  (0.31267)  (0.12997)  (0.17282)  (0.23749)
[-2.09323] [-0.09526] [-1.86962] [ 2.63059]
   
C  0.171800  0.047345  0.087647  0.018586
  (0.04798)  (0.01995)  (0.02652)  (0.03645)
[ 3.58032] [ 2.37370] [ 3.30466] [ 0.50994]
   
R-squared  0.413443  0.529649  0.813137  0.406584
Adj. R-squared  0.102913  0.280639  0.714209  0.092423
Sum sq. resids  0.051536  0.008904  0.015744  0.029733
S.E. equation  0.055059  0.022886  0.030433  0.041821
F-statistic  1.331410  2.127023  8.219522  1.294191
Log likelihood  46.21646  69.91872  62.22459  53.64175
Akaike AIC -2.682701 -4.438424 -3.868488 -3.232722
Schwarz SC -2.202761 -3.958484 -3.388549 -2.752783
Mean dependent  0.155863  0.044158  0.142493  0.024676
S.D. dependent  0.058132  0.026984  0.056927  0.043899
   
Determinant resid covariance (dof adj.)   1.54E-12  
Determinant resid covariance   2.43E-13  
Log likelihood   238.8846  
Akaike information criterion  -14.43590  
Schwarz criterion  -12.32417