VAR.Wald with Two Types of Error Messsages

Question

I am trying to identify the granger causality from one data A to data B.

data A and data B are the left and right hand sides within the cbind, respectively.

So I decided to use 'VAR.Wald' from 'VAR.etp' R package to do this work.

Case 1) If I get errors due to missing values for some data points, I understand this cannot be granger causing the relationship, so I am willing to denote them as zero granger cause.

Case 2) But I am not sure whether I should regard as zero granger cause even if the message says singularity error message. If this also means zero granger cause as well, then I could simply ignore the message regarding as zero granger cause.

To understand these, I would like to confirm whether my understanding is correct for each case 1 and 2.

For case 1, I have run the following code with such data input and got the corresponding error message.

> library(VAR.etp)

> restrict = rbind(c(1,1,2),c(2,1,2))
> p1<-VAR.select(cbind(data[1:36,1],data[1:36,2]), type = "const", ic = "aic", 8)$p
> VAR.Wald(cbind(data[1:36,1],data[1:36,2]), p = p1,restrict,type="const")
Error in solve.default(mat2) : 
  Lapack routine dgesv: system is exactly singular: U[2,2] = 0


> cbind(data[1:36,1],data[1:36,2])
              [,1]         [,2]
 [1,]  0.000981476  0.000558361
 [2,]  0.011151409 -0.003257835
 [3,]  0.004557608 -0.006943921
 [4,]  0.006205915 -0.013679118
 [5,] -0.001042010 -0.008169046
 [6,] -0.000386637 -0.010975501
 [7,]  0.000265246 -0.010123251
 [8,]  0.001653729 -0.011535562
 [9,] -0.000907953 -0.007163644
[10,]  0.003093070 -0.009980498
[11,] -0.000466416 -0.012855000
[12,]  0.000109385 -0.015136829
[13,] -0.007933718 -0.007598663
[14,]  0.001733839 -0.008174362
[15,]  0.005250601 -0.008150644
[16,]  0.000433372 -0.006254862
[17,]  0.003808044 -0.007596417
[18,] -0.001854051 -0.008345769
[19,] -0.003392373 -0.015398453
[20,]  0.007375488 -0.018780131
[21,]  0.005362482 -0.017121529
[22,]  0.003666440 -0.017543625
[23,]  0.001129405 -0.014864303
[24,]  0.003299308 -0.016879411
[25,]  0.003666255 -0.019606721
[26,] -0.000924321 -0.013269746
[27,]  0.003038604 -0.009391309
[28,]  0.002231664 -0.012697503
[29,] -0.002151253 -0.010437314
[30,]  0.002374940 -0.008493108
[31,] -0.001372043 -0.006795869
[32,]  0.001088183 -0.006081438
[33,] -0.004639136 -0.001357142
[34,]  0.005875748 -0.003310532
[35,]  0.002968681 -0.007861249
[36,]  0.003829430 -0.005731017

Likewise, for case 2, I have run the following code with such data input and got the corresponding error message.

> library(VAR.etp)

> restrict = rbind(c(1,1,2),c(2,1,2))
> p1<-VAR.select(cbind(data[11:46,1],data[11:46,4]), type = "const", ic = "aic", 8)$p
> VAR.Wald(cbind(data[11:46,1],data[11:46,4]), p = p1,restrict,type="const")
Error in matrix(0, nrow = p * (k^2) + add * k) : 
  invalid 'nrow' value (too large or NA)


> cbind(data[11:46,1],data[11:46,4])
               [,1]          [,2]
 [1,] -0.0004664160            NA
 [2,]  0.0001093850            NA
 [3,] -0.0079337180            NA
 [4,]  0.0017338390            NA
 [5,]  0.0052506010            NA
 [6,]  0.0004333720            NA
 [7,]  0.0038080440            NA
 [8,] -0.0018540510            NA
 [9,] -0.0033923730            NA
[10,]  0.0073754880            NA
[11,]  0.0053624820            NA
[12,]  0.0036664400            NA
[13,]  0.0011294050            NA
[14,]  0.0032993080            NA
[15,]  0.0036662550            NA
[16,] -0.0009243210            NA
[17,]  0.0030386040            NA
[18,]  0.0022316640            NA
[19,] -0.0021512530            NA
[20,]  0.0023749400            NA
[21,] -0.0013720430            NA
[22,]  0.0010881830            NA
[23,] -0.0046391360            NA
[24,]  0.0058757480            NA
[25,]  0.0029686810            NA
[26,]  0.0038294300  0.0015363910
[27,]  0.0000677289  0.0064068430
[28,] -0.0027870820 -0.0000226565
[29,]  0.0030883610  0.0187701850
[30,]  0.0061435630 -0.0031117120
[31,]  0.0001360900  0.0096137050
[32,] -0.0024424050 -0.0140845140
[33,] -0.0052592710  0.0042199480
[34,] -0.0018727950  0.0047308980
[35,] -0.0103380180 -0.0396535230
[36,]  0.0093877890  0.0377174650

Could you please confirm whether the codes' running are correct and whether my understanding & approach are correct?