# Bootstrapping wald test scores following SUR with panel data

#### Daan Bovenberg

##### New Member
Hey everyone. Can anyone help me implement these steps into R code?

http://i.stack.imgur.com/y7czu.png[/img] Using this system: I found this piece of R code which might be 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))
P_val2 <-mean( abs(BtStrpRes[,2] - mean(BtStrpRes[,2]) )> abs( Betas))
P_val3 <-mean( abs(BtStrpRes[,3] - mean(BtStrpRes[,3]) )> abs( Betas))

#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))

I think something went wrong on creation of an earlier similar topic, so that's why I created another.