写毕业论文，不知道怎么解释两个协整关系的vecm模型，看了很多论文都是一个协整关系的。还有就是误差修正项好像一正一负，是这样的吗，不是得为负吗。有大哥帮我看看吗，我把数据也放上来了，能帮我看看因果检验通过吗，我自己做的是滞后6阶在10%置信度下，x1,x2,x3分别是Y的格兰杰原因
Error Correction: D(X1) D(X2) D(X3) D(Y)

CointEq1 -0.355758 0.107540 -0.043189 0.012785
(0.08835) (0.07201) (0.05845) (0.70836)
[-4.02678] [ 1.49332] [-0.73895] [ 0.01805]

CointEq2 1.307483 -0.757225 0.608337 -0.375082
(0.29640) (0.24160) (0.19609) (2.37652)
[ 4.41115] [-3.13415] [ 3.10240] [-0.15783]

D(X1(-1)) -0.084504 0.328056 -0.336137 0.112960
(0.17363) (0.14153) (0.11487) (1.39217)
[-0.48668] [ 2.31789] [-2.92630] [ 0.08114]

D(X1(-2)) -0.242694 0.479186 -0.414385 -0.351627
(0.18944) (0.15441) (0.12532) (1.51887)
[-1.28113] [ 3.10327] [-3.30658] [-0.23151]

D(X1(-3)) -0.317701 0.224392 -0.175887 0.034582
(0.17630) (0.14371) (0.11663) (1.41356)
[-1.80203] [ 1.56146] [-1.50805] [ 0.02446]

D(X2(-1)) 0.129887 3.013608 -2.742879 11.24399
(0.79792) (0.65040) (0.52787) (6.39762)
[ 0.16278] [ 4.63346] [-5.19617] [ 1.75753]

D(X2(-2)) -1.401563 1.809460 -1.994386 -1.632569
(0.80281) (0.65439) (0.53110) (6.43681)
[-1.74582] [ 2.76513] [-3.75521] [-0.25363]

D(X2(-3)) -0.635277 1.658877 -1.827210 -9.085861
(0.65787) (0.53624) (0.43521) (5.27468)
[-0.96566] [ 3.09353] [-4.19844] [-1.72254]

D(X3(-1)) 0.066968 3.024416 -2.621548 9.535340
(0.76367) (0.62248) (0.50521) (6.12300)
[ 0.08769] [ 4.85864] [-5.18907] [ 1.55730]

D(X3(-2)) -1.818255 1.178335 -1.220247 -0.625617
(0.73136) (0.59614) (0.48383) (5.86389)
[-2.48614] [ 1.97660] [-2.52207] [-0.10669]

D(X3(-3)) -0.366061 0.937218 -1.016375 -6.074983
(0.59305) (0.48340) (0.39233) (4.75495)
[-0.61725] [ 1.93880] [-2.59062] [-1.27761]

D(Y(-1)) 0.025637 -0.058836 0.065051 -0.094804
(0.03132) (0.02553) (0.02072) (0.25114)
[ 0.81849] [-2.30446] [ 3.13938] [-0.37750]

D(Y(-2)) 0.076526 -0.093894 0.091803 0.207710
(0.03259) (0.02656) (0.02156) (0.26128)
[ 2.34831] [-3.53481] [ 4.25837] [ 0.79496]

D(Y(-3)) 0.060900 -0.014045 0.002180 -0.046145
(0.02481) (0.02022) (0.01641) (0.19893)
[ 2.45454] [-0.69449] [ 0.13283] [-0.23196]

R-squared 0.628532 0.697099 0.786506 0.720938
Adj. R-squared 0.306593 0.434585 0.601478 0.479084
Sum sq. resids 0.002873 0.001909 0.001257 0.184663
S.E. equation 0.013838 0.011280 0.009155 0.110954
F-statistic 1.952333 2.655474 4.250741 2.980886
Log likelihood 92.53870 98.46697 104.5207 32.17031
Akaike AIC -5.416462 -5.825308 -6.242807 -1.253124
Schwarz SC -4.756388 -5.165234 -5.582733 -0.593051
Mean dependent 0.018810 -0.000600 0.006414 -0.020317
S.D. dependent 0.016619 0.015001 0.014502 0.153731

Determinant resid covariance (dof adj.) 7.03E-16
Determinant resid covariance 5.03E-17
Log likelihood 379.5590
Akaike information criterion -21.62476
Schwarz criterion -18.51298
Number of coefficients 66