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The code given below estimates a VEC model with 4 cointegrating vectors. It is a reproducible code, so just copy and paste into your R console (or script editor).

nobs = 200
e = rmvnorm(n=nobs,sigma=diag(c(.5,.5,.5,.5,.5)))
e1.ar1 = arima.sim(model=list(ar=.75),nobs,innov=e[,1])
e2.ar1 = arima.sim(model=list(ar=.75),nobs,innov=e[,2])
e3.ar1 = arima.sim(model=list(ar=.75),nobs,innov=e[,3])
e4.ar1 = arima.sim(model=list(ar=.75),nobs,innov=e[,4])
y5 = cumsum(e[,5])
y1 = y5 + e1.ar1
y2 = y5 + e2.ar1
y3 = y5 + e3.ar1
y4 = y5 + e4.ar1
data = cbind(y1,y2,y3,y4,y5)

jcointt = ca.jo(data,ecdet="const",type="trace",K=2,spec="transitory")
summary(jcointt)

vecm <- cajorls(jcointt,r=4)
summary(vecm$rlm)
print(vecm)

The cointegrating vectors you will get from this estimation will look like this:

            ect1   ect2  ect3   ect4
y1.l1        1      0      0     0
y2.l1        0      1      0     0
y3.l1        0      0      1     0
y4.l1        0      0      0     1
y5.l1      b4.1    b4.2   b4.3   b4.4
constant    c1      c2     c3    c4

here, b4.1 through to b4.4 are the coefficients of the 4 cointegrating equations (ecm's) ( which are $\beta's$). Similarly, $c's$ are the intercepts of the cointegrating equation.

As you can see, there are r-1 restrictions, that's 3 restrictions, on each equation by default. I want to put restrictions on these equations matrix and re-estimate the VECM so that I will have the following matrix of long run equations:

            ect1   ect2  ect3   ect4
y1.l1        1      0      0     0
y2.l1      b1.1     1      0     0
y3.l1      b2.1     0      1     0
y4.l1      b3.1     0      0     1
y5.l1      b4.1    b4.2   b4.3   b4.4
constant    c1      c2     c3    c4

From this output, I want to extract the first equation ect1 for inference. It should look like this: $y_1=\beta_0-\beta_1y_2-\beta_2y_3-\beta_3y_4-\beta_4y_5$

What I do not know is how to get from the first long run equations matrix to the second one. Basically, I do not know how to construct the restrictions matrix on $\beta$.

Any suggestions and hints are welcome and appreciated!

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