Bootstrapping test statistics with panel data and SUR

#1
As a newbie to bootstrapping, I'm having some trouble with developing with critical wald test scores for every observation in my panel dataset. I'm using a Seemingly Unrelated Regression to predict residuals, but then I'm unsure how to resample these and develop these critical wald test values. I know how to use to basics of the bootstrap command in stata, but I dont how to develop these waldscores.

These are the steps I want to implement:





Using this system:



I found the following piece of code in R, which develops p-values with bootstrapping, in another topic on this website which might helpful:
Code:
# Sample Size
N           <- 2^12;
# Linear Model to Boostrap          
Model2Boot  <- lm( mpg ~ wt + disp, mtcars)
# Values of the model coefficients
Betas       <- coefficients(Model2Boot)
# Number of coefficents to test against
M           <- length(Betas)
# Matrix of M columns to hold Bootstraping results
BtStrpRes   <- matrix( rep(0,M*N), ncol=M)

for (i in 1:N) {
# Simulate data N times from the model we assume be true
# and save the resulting coefficient in the i-th row of BtStrpRes
BtStrpRes[i,] <-coefficients(lm(unlist(simulate(Model2Boot)) ~wt + disp, mtcars))
}

#Get the p-values for coefficient
P_val1 <-mean( abs(BtStrpRes[,1] - mean(BtStrpRes[,1]) )> abs( Betas[1]))
P_val2 <-mean( abs(BtStrpRes[,2] - mean(BtStrpRes[,2]) )> abs( Betas[2]))
P_val3 <-mean( abs(BtStrpRes[,3] - mean(BtStrpRes[,3]) )> abs( Betas[3]))

#and some parametric bootstrap confidence intervals (2.5%, 97.5%) 
ConfInt1 <- quantile(BtStrpRes[,1], c(.025, 0.975))
ConfInt2 <- quantile(BtStrpRes[,2], c(.025, 0.975))
ConfInt3 <- quantile(BtStrpRes[,3], c(.025, 0.975))
How do I modify this piece of code to create panelvariable-specific wald test scores?