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雷达卡
本人真的是小白，有些地方真的是看着头疼，希望有大神帮忙看一下，感谢感谢！！
我做的这个模型X、Y是收入和支出，然后eviews做出的结果如下
Date: 03/07/20 Time: 21:05
Sample (adjusted): 1985 2019
Included observations: 35 after adjustments
Trend assumption: Linear deterministic trend
Series: X Y
Lags interval (in first differences): 1 to 4
Unrestricted Cointegration Rank Test (Trace)
Hypothesized Trace 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None * 0.536071 34.75632 15.49471 0.0000
At most 1 * 0.201494 7.875469 3.841466 0.0050
Trace test indicates 2 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized Max-Eigen 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None * 0.536071 26.88085 14.26460 0.0003
At most 1 * 0.201494 7.875469 3.841466 0.0050
Max-eigenvalue test indicates 2 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegrating Coefficients (normalized by b'*S11*b=I):
X Y
0.011553 -0.019982
0.019661 -0.029711
Unrestricted Adjustment Coefficients (alpha):
D(X) -72.62232 -11.31893
D(Y) -62.73010 14.34978
1 Cointegrating Equation(s): Log likelihood -386.2322
Normalized cointegrating coefficients (standard error in parentheses)
X Y
1.000000 -1.729618
(0.03504)
Adjustment coefficients (standard error in parentheses)
D(X) -0.839013
(0.16662)
D(Y) -0.724727
(0.15375)
【下面这个是做出来带有C的】
Vector Error Correction Estimates
Date: 03/07/20 Time: 21:17
Sample (adjusted): 1986 2019
Included observations: 34 after adjustments
Standard errors in ( ) & t-statistics in [ ]
Cointegrating Eq: CointEq1
X(-1) 1.000000
Y(-1) -1.640004
(0.02333)
[-70.3073]
C 695.2547
Error Correction: D(X) D(Y)
CointEq1 -1.708119 -0.676531
(0.40609) (0.42748)
[-4.20629] [-1.58261]
D(X(-1)) 2.295277 1.719568
(0.36382) (0.38298)
[ 6.30889] [ 4.48997]
D(X(-2)) 0.134396 -0.869909
(0.53226) (0.56030)
[ 0.25250] [-1.55258]
D(X(-3)) 1.579681 0.531700
(0.37791) (0.39782)
[ 4.18000] [ 1.33653]
D(X(-4)) -0.827369 -0.640773
(0.31543) (0.33205)
[-2.62298] [-1.92977]
D(X(-5)) -0.084564 0.324285
(0.29371) (0.30918)
[-0.28792] [ 1.04885]
D(Y(-1)) -2.397081 -1.046826
(0.71702) (0.75479)
[-3.34311] [-1.38691]
D(Y(-2)) -1.989090 -0.389719
(0.71269) (0.75023)
[-2.79097] [-0.51947]
D(Y(-3)) -1.753264 -0.811824
(0.49498) (0.52105)
[-3.54208] [-1.55804]
D(Y(-4)) -0.148383 0.369144
(0.35486) (0.37355)
[-0.41815] [ 0.98821]
D(Y(-5)) -0.451356 -0.022021
(0.30034) (0.31616)
[-1.50283] [-0.06965]
C 1065.851 427.7329
(250.277) (263.460)
[ 4.25869] [ 1.62352]
R-squared 0.977127 0.950837
Adj. R-squared 0.965690 0.926256
Sum sq. resids 163894.0 181615.0
S.E. equation 86.31179 90.85828
F-statistic 85.43821 38.68107
Log likelihood -192.4144 -194.1597
Akaike AIC 12.02437 12.12704
Schwarz SC 12.56309 12.66576
Mean dependent 510.7941 352.5588
S.D. dependent 465.9723 334.5801
Determinant resid covariance (dof adj.) 20862074
Determinant resid covariance 8734640.
Log likelihood -368.1955
Akaike information criterion 23.18797
Schwarz criterion 24.35519
想知道他的 协整方程怎么写QAQ，（心里有个小答案，但是觉得和类似数据差距太大）辛苦大神帮我看一下！（鞠躬）
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