Actual StataMP numerical output. Log header and machine paths omitted.

. * BEGIN short
. regress y x, vce(robust)

Linear regression                               Number of obs     =      4,000
                                                F(1, 3998)        =   11775.50
                                                Prob > F          =     0.0000
                                                R-squared         =     0.7514
                                                Root MSE          =     1.2294

------------------------------------------------------------------------------
             |               Robust
           y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
           x |   1.516317   .0139733   108.51   0.000     1.488921    1.543712
       _cons |   .9947365   .0194616    51.11   0.000     .9565808    1.032892
------------------------------------------------------------------------------

. * END short
. record biased x

. * BEGIN controlled
. regress y x ability, vce(robust)

Linear regression                               Number of obs     =      4,000
                                                F(2, 3997)        =   10761.78
                                                Prob > F          =     0.0000
                                                R-squared         =     0.8390
                                                Root MSE          =     .98934

------------------------------------------------------------------------------
             |               Robust
           y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
           x |   .9976925   .0159362    62.61   0.000     .9664487    1.028936
     ability |   1.031297   .0224454    45.95   0.000     .9872913    1.075302
       _cons |   .9881499   .0156569    63.11   0.000     .9574537    1.018846
------------------------------------------------------------------------------

. * END controlled
. record controlled x

. generate double clean_y=1+x+rnormal()

. generate double noisy_x=x+sqrt(2)*rnormal()

. * BEGIN measurement
. regress clean_y noisy_x, vce(robust)

Linear regression                               Number of obs     =      4,000
                                                F(1, 3998)        =    2103.91
                                                Prob > F          =     0.0000
                                                R-squared         =     0.3458
                                                Root MSE          =     1.4013

------------------------------------------------------------------------------
             |               Robust
     clean_y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     noisy_x |   .5177554   .0112879    45.87   0.000     .4956249    .5398859
       _cons |   1.045476   .0221565    47.19   0.000     1.002037    1.088915
------------------------------------------------------------------------------

. * END measurement
. record measurement noisy_x

. preserve

. capture program drop mcdraw

. program define mcdraw, rclass
  1.     syntax, N(integer)
  2.     clear
  3.     set obs `n'
  4.     generate double a=rnormal()
  5.     generate double x=a+rnormal()
  6.     generate double y=1+x+a+rnormal()
  7.     quietly regress y x
  8.     return scalar short=_b[x]
  9.     quietly regress y x a
 10.     return scalar adjusted=_b[x]
 11. end

. simulate b_short=r(short) b_adjusted=r(adjusted), reps(300) seed(6030610) nodots: mcdraw, n(100)

      Command: mcdraw, n(100)
      b_short: r(short)
   b_adjusted: r(adjusted)


. generate n=100

. tempfile small

. save `small'
file /var/folders/77/1vjhzbsn76q82b04p34nzxjm0000gn/T//St62962.000002 saved as .dta format

. simulate b_short=r(short) b_adjusted=r(adjusted), reps(300) seed(6030611) nodots: mcdraw, n(1000)

      Command: mcdraw, n(1000)
      b_short: r(short)
   b_adjusted: r(adjusted)


. generate n=1000

. append using `small'

. export delimited using "output/meeting_06_simulation.csv", replace
(file output/meeting_06_simulation.csv not found)
file output/meeting_06_simulation.csv saved

. restore

. 
. postclose results

. use "output/meeting_06_estimates.dta", clear

. export delimited using "output/meeting_06_estimates.csv", replace
(file output/meeting_06_estimates.csv not found)
file output/meeting_06_estimates.csv saved

. display "ECO603_MEETING_06_COMPLETE"
ECO603_MEETING_06_COMPLETE

