# This is the first Monte-Carlo
# Convergence for Sample Covariance
# Set the working directory with File > Working Directory

# *************** Before the loop *********************
nulldata 5000
set seed 547
matrix S = {1,0.5;0.5,1}
matrix A = cholesky(S)

#****** open a loop, to be repeated R=500 times **********
loop 500 --progressive --quiet
 genr u1 = normal()
 genr u2 = normal()
 genr x1 = A[1,1]*u1+A[1,2]*u2
 genr x2 = A[2,1]*u1+A[2,2]*u2
 smpl 5 --random
 genr cov5 = cov(x1,x2)
 smpl full
 smpl 50 --random
 genr cov50 = cov(x1,x2)
 smpl full
 smpl 500 --random
 genr cov500 = cov(x1,x2)
 smpl full
 genr cov5000 = cov(x1,x2)
 store myfirstMC.gtd cov5 cov50 cov500 cov5000
endloop

#*********** we open the results ************************

open myfirstMC.gtd
summary cov* --simple