Monte Carlo simulations for VAR models - r

Question

I've been trying to estimate VAR models using Monte Carlo Simulation. I have 3 endogenous variables. I need some guidance regarding this.
First of all, I want to add an outlier as a percentage of the sample size.
Second (second simulation for same model), I want to add multivariate contaminated normal distribution like 0.9N (0, I) + 0.1((0,0,0)',(100, 100, 100)) instead of outlier.
Could you tell me how to do these?
Thank you.
RR <- function(n, out){
# n is number of observations
k <- 3 # Number of endogenous variables
p <- 2 # Number of lags
# add outlier
n[1]<- n[1]+out
# Generate coefficient matrices
B1 <- matrix(c(.1, .3, .4, .1, -.2, -.3, .03, .1, .1), k) # Coefficient matrix of lag 1
B2 <- matrix(c(0, .2, .1, .07, -.4, -.1, .5, 0, -.1), k) # Coefficient matrix of lag 2
M <- cbind(B1, B2) # Companion form of the coefficient matrices
# Generate series
DT <- matrix(0, k, n + 2*p) # Raw series with zeros
for (i in (p + 1):(n + 2*p)){ # Generate series with e ~ N(0,1)
DT[, i] <- B1%*%DT[, i-1] + B2%*%DT[, i-2] + rnorm(k, 0, 1)
}
DT <- ts(t(DT[, -(1:p)])) # Convert to time series format
#names <- c("V1", "V2", "V3") # Rename variables
colnames(DT) <- c("Y1", "Y2", "Y3")
#plot.ts(DT) # Plot the series
# estimate VECM
vecm1 <- VECM(DT, lag = 2, r = 2, include = "const", estim ="ML")
vecm2 <- VECM(DT, lag = 2, r = 1, include = "const", estim ="ML")
# mse
mse1 <- mean(vecm1$residuals^2)
mse2 <- mean(vecm2$residuals^2)
#param_list <- unname(param_list)
return(list("mse1" = mse1, "mse2" = mse2, "mse3" = mse3))
}
# defined the parameter grids(define the parameters ranges we want to run our function with)
n_grid = c(50, 80, 200, 400)
out_grid = c(0 ,5, 10)
# collect parameter grids in a list (to enter it into the Monte Carlo function)
prml = list("n" = n_grid, "out" = out_grid)
# run simulation
RRS <- MonteCarlo(func = RR, nrep = 1000, param_list = prml)
summary(RRS)
# make table:
rows = "n"
cols = "out"
MakeTable(output = RRS, rows = rows, cols = cols)