How can i analyze the result of VECM model

[ld9g11]

My topic is about the impact of exchange rate volatility, exchange rate, gap on the export.
Therefore, my model is lnt=lnRER+lnGDP+lnv
Following is the result of VECM model. But i am not sure how to analyze/interpret the error correction term and the lag term.
Q1:How can i interpret the error correction coefficient in my model?
Q2: How to interpret the D(LNT(-1)) D(LNRER(-1)) D(LNGDP(-1)) D(LLNV(-1)) ? It seems they are all insignificant? So Does it means all the independent variables has no impact on export in the short run?
Q3: Should i look the coefficient of error correction for D(LNGDP), it is -2.01124, which is significant. But i just want to do the research of the impact of exchange volatility,exchange rate, gap on the export, is it necessary to talk about this item?


Vector Error Correction Estimates
Date: 08/06/12 Time: 19:24
Sample (adjusted): 3 28
Included observations: 26 after adjustments
Standard errors in ( ) & t-statistics in [ ]


Cointegrating Eq: CointEq1


LNT(-1) 1.000000

LNRER(-1) -1.710679
(0.59660)
[-2.86736]

LNGDP(-1) -2.946167
(0.50532)
[-5.83034]

LNV(-1) 0.390728
(0.12602)
[ 3.10047]

C 8.923472


Error Correction: D(LNT) D(LNRER) D(LNGDP) D(LNV)


CointEq1 -0.269722 -0.082514 -0.022286 -0.223939
(0.07625) (0.06840) (0.01108) (0.58843)
[-3.53717] [-1.20640] [-2.01124] [-0.38057]

D(LNT(-1)) 0.040456 -0.047681 -0.054227 0.173617
(0.38609) (0.34631) (0.05610) (2.97935)
[ 0.10478] [-0.13768] [-0.96654] [ 0.05827]

D(LNRER(-1)) -0.170088 -0.032448 0.022333 0.227879
(0.43830) (0.39314) (0.06369) (3.38224)
[-0.38806] [-0.08253] [ 0.35065] [ 0.06738]

D(LNGDP(-1)) -1.477159 -0.017701 0.424343 -2.940721
(1.76896) (1.58670) (0.25705) (13.6506)
[-0.83505] [-0.01116] [ 1.65079] [-0.21543]

D(LNV(-1)) 0.049284 0.007425 0.002788 -0.402691
(0.03407) (0.03056) (0.00495) (0.26287)
[ 1.44674] [ 0.24300] [ 0.56317] [-1.53188]

C 0.188641 0.012055 0.023237 -0.006355
(0.05509) (0.04941) (0.00800) (0.42509)
[ 3.42447] [ 0.24397] [ 2.90283] [-0.01495]


R-squared 0.499226 0.106996 0.326088 0.242529
Adj. R-squared 0.374033 -0.116255 0.157610 0.053161
Sum sq. resids 0.255820 0.205822 0.005402 15.23364
S.E. equation 0.113097 0.101445 0.016435 0.872744
F-statistic 3.987636 0.479263 1.935494 1.280728
Log likelihood 23.18552 26.01255 73.33584 -29.94273
Akaike AIC -1.321963 -1.539427 -5.179680 2.764825
Schwarz SC -1.031633 -1.249097 -4.889350 3.055155
Mean dependent 0.151140 0.002833 0.025600 -0.028513
S.D. dependent 0.142947 0.096017 0.017906 0.896910


Determinant resid covariance (dof adj.) 2.76E-09
Determinant resid covariance 9.65E-10
Log likelihood 122.2967
Akaike information criterion -7.253591
Schwarz criterion -5.898718
ld9g11


As my supervisor didn't supervise me anymore, and i am not familiar with this method, so i will be very appreciate your help if someone know how to analyze.