VEC模型结果怎么理解？-经管之家官网！

VEC估计结果如下，不知道如何写协整方程，因为有三个协整关系，所以又三个方程，但是出来的结果不知道要怎么写，还有VEC方程怎么写也不清楚，误差修正项的系数是多少、检验结果是否显著，要怎么看呢？小弟是计量新手，有很多不懂的，望各位大侠帮忙解答一下，不尽感激(*^__^*)
Vector Error Correction Estimates
Date: 07/25/12 Time: 22:14
Sample (adjusted): 1983 2010
Included observations: 28 after adjustments
Standard errors in ( ) & t-statistics in [ ]

Cointegrating Eq:CointEq1 CointEq2 CointEq3

LNGUI(-1)1.0000000.0000000.000000

LNPGDP(-1)0.0000001.0000000.000000

LNPGDP2(-1)0.0000000.0000001.000000

LNSSL(-1) -0.115329 -0.402501 -4.618520
(0.03458)(0.07285)(1.08461)
[-3.33535] [-5.52524] [-4.25824]

C1.060205 -7.138005 -52.03107

Error Correction: D(LNGUI) D(LNPGDP) D(LNPGDP2) D(LNSSL)

CointEq10.1074140.68454712.046440.015450
(0.98731)(0.47944)(7.25194)(3.33017)
[ 0.10880] [ 1.42782] [ 1.66113] [ 0.00464]

CointEq2 -0.366021 -1.686595 -28.0956511.23536
(1.84028)(0.89364)(13.5172)(6.20724)
[-0.19889] [-1.88733] [-2.07851] [ 1.81004]

CointEq30.0552300.1274392.160691 -0.745645
(0.14847)(0.07210)(1.09055)(0.50079)
[ 0.37199] [ 1.76759] [ 1.98129] [-1.48893]

D(LNGUI(-1)) -0.790092 -0.154483 -4.045586 -2.254520
(0.75179)(0.36507)(5.52204)(2.53578)
[-1.05095] [-0.42316] [-0.73263] [-0.88908]

D(LNGUI(-2)) -0.296292 -0.577430 -9.704079 -0.167243
(0.76454)(0.37126)(5.61571)(2.57880)
[-0.38754] [-1.55531] [-1.72802] [-0.06485]

D(LNGUI(-3)) -0.469817 -0.397637 -6.4135321.546013
(0.47784)(0.23204)(3.50985)(1.61176)
[-0.98320] [-1.71365] [-1.82730] [ 0.95921]

D(LNGUI(-4)) -0.489109 -0.035354 -1.093235 -1.386087
(0.39871)(0.19361)(2.92857)(1.34483)
[-1.22674] [-0.18260] [-0.37330] [-1.03068]

D(LNPGDP(-1)) -4.89779611.02444172.7176 -47.14145
(12.4169)(6.02966)(91.2044)(41.8821)
[-0.39445] [ 1.82837] [ 1.89374] [-1.12558]

D(LNPGDP(-2))13.754921.92818944.459092.964546
(6.21096)(3.01605)(45.6206)(20.9495)
[ 2.21462] [ 0.63931] [ 0.97454] [ 0.14151]

D(LNPGDP(-3)) -1.8215400.00764426.30803 -14.91387
(9.03764)(4.38868)(66.3830)(30.4838)
[-0.20155] [ 0.00174] [ 0.39631] [-0.48924]

D(LNPGDP(-4))17.0094517.72396274.6850 -75.06875
(18.7244)(9.09258)(137.534)(63.1572)
[ 0.90841] [ 1.94928] [ 1.99722] [-1.18860]

D(LNPGDP2(-1))0.249853 -0.688433 -10.866602.725714
(0.82662)(0.40141)(6.07169)(2.78819)
[ 0.30226] [-1.71504] [-1.78972] [ 0.97759]

D(LNPGDP2(-2)) -0.899996 -0.152722 -3.403368 -0.254837
(0.44812)(0.21761)(3.29154)(1.51151)
[-2.00837] [-0.70182] [-1.03397] [-0.16860]

D(LNPGDP2(-3))0.104274 -0.013767 -2.1333160.895527
(0.67017)(0.32543)(4.92250)(2.26047)
[ 0.15559] [-0.04230] [-0.43338] [ 0.39617]

D(LNPGDP2(-4)) -1.303069 -1.248584 -19.366264.949825
(1.34053)(0.65096)(9.84646)(4.52161)
[-0.97205] [-1.91805] [-1.96682] [ 1.09470]

D(LNSSL(-1))0.0662060.0648691.258419 -0.144101
(0.16693)(0.08106)(1.22616)(0.56307)
[ 0.39660] [ 0.80022] [ 1.02631] [-0.25592]

D(LNSSL(-2))0.1068950.0879061.470952 -0.334916
(0.11219)(0.05448)(0.82402)(0.37840)
[ 0.95283] [ 1.61361] [ 1.78508] [-0.88508]

D(LNSSL(-3))0.1529870.0252630.4865760.067724
(0.06452)(0.03133)(0.47393)(0.21764)
[ 2.37105] [ 0.80630] [ 1.02667] [ 0.31118]

D(LNSSL(-4))0.1049910.0059100.1459980.036945
(0.04602)(0.02235)(0.33806)(0.15524)
[ 2.28119] [ 0.26443] [ 0.43187] [ 0.23798]

C0.2050330.0434880.8770571.428631
(0.17079)(0.08294)(1.25449)(0.57607)
[ 1.20049] [ 0.52435] [ 0.69914] [ 2.47994]

R-squared0.8754330.8936440.8982340.947941
Adj. R-squared0.5795850.6410470.6565380.824299
Sum sq. resids0.0080250.0018920.4329590.091300
S.E. equation0.0316720.0153800.2326370.106829
F-statistic2.9590643.5378333.7163897.666861
Log likelihood74.4733794.6996818.6401440.43100
Akaike AIC -3.890955 -5.3356910.097133 -1.459357
Schwarz SC -2.939380 -4.3841171.048707 -0.507782
Mean dependent0.0166230.0877251.2989920.098572
S.D. dependent0.0488470.0256710.3969530.254862

Determinant resid covariance (dof adj.) 2.30E-14
Determinant resid covariance 1.53E-16
Log likelihood 350.9083
Akaike information criterion-18.49345
Schwarz criterion-14.11621