# Set the sample size and save it in n
nulldata 100
scalar n = $nobs

# generate n observations on x   
series z = 5*uniform()
# set a seed if you want to get same results each time you run this
set seed 3213799
# Set the values of the parameters
scalar slope = 1  # regression slope
scalar sigma = 10 # measurement error in y
scalar sigx = .1  # amount of measurement error in x
scalar gam = 1    # instrument strength
scalar rho = .0   # correlation between error 
scalar n = $nobs

# start the loop, indicating the desired number of samples.
loop 400 --progressive --quiet
   series u = normal(0,sigma) 
   # generate sample of x and y 
   series x=gam*z+rho*u+normal(0,sigx)
   series y = slope*x + u   
   # run the ols regression 
   ols y const x 
   # save the estimated coefficients   
   genr b2 = $coeff(x)
   genr s2 = $stderr(x)
   # run the tsls regression 
   tsls y const x; const z
   genr biv2 = $coeff(x)
   genr siv2 = $stderr(x)
   # store the results in a dataset for future analysis is desired
   store arcoeff.gdt b2 biv2 s2 siv2
   print b2 biv2 s2 siv2
endloop