set verbose off
nulldata 250

K = 5000
alpha = 0.05
x = normal()

matrix rej = zeros(1, 4)

loop i=1..K
   # generate the error term
   u = normal()
   # generate the dependent var
   y = 1 + u
   
   # use all 250 observations
   ols y 0 x --quiet
   tstat = $coeff[2] / $stderr[2]
   # two tailed test
   if 2 * pvalue(z, abs(tstat)) < alpha
      rej[1] += 1
   endif
   if 2 * pvalue(t, $df, abs(tstat)) < alpha
      rej[2] += 1
   endif

   # use only the first 20 observations   
   smpl 1 20
   ols y 0 x --quiet
   tstat = $coeff[2] / $stderr[2]
   # two tailed test
   if 2 * pvalue(z, abs(tstat)) < alpha
      rej[3] += 1
   endif
   if 2 * pvalue(t, $df, abs(tstat)) < alpha
      rej[4] += 1
   endif
   # reset to full sample range
   smpl full
endloop

rej *= 100/K
printf "Rejection rates at alpha = %g:\n", alpha
printf "n = 250, using z: %.2f%%\n", rej[1]
printf "n = 250, using t: %.2f%%\n", rej[2]
printf "n =  20, using z: %.2f%%\n", rej[3]
printf "n =  20, using t: %.2f%%\n", rej[4]