How to estimate RMSE after bioprobit estimation?

Dear all,

I have used STATA to estimate Seemingly unrelated bivariate ordered probit regression model. I realized that the ereturn list does not have information about either the mse or rmse. To compare this model with other models, I have to manually compute the estimates.

Could anyone kindly assist me with this task?

I first run the code below:

Code:

bioprobit (dnmcas=sq noncomply headon daytime weekday sideswipe nrintersec) (daccdttyp=nrintersec daytime noncomply othervio dens)

Then, this was the result of the estimation.

Code:

group(daccd |
      ttyp) |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        285       19.18       19.18
          2 |      1,201       80.82      100.00
------------+-----------------------------------
      Total |      1,486      100.00

initial:       log likelihood = -1134.8348
rescale:       log likelihood = -1134.8348
rescale eq:    log likelihood = -1121.0874
Iteration 0:   log likelihood = -1121.0874  
Iteration 1:   log likelihood = -1121.0282  
Iteration 2:   log likelihood = -1121.0282  

Seemingly unrelated bivariate ordered probit regression

                                                Number of obs     =      1,486
                                                Wald chi2(7)      =      63.14
Log likelihood = -1121.0282                     Prob > chi2       =     0.0000

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dnmcas       |
          sq |   .1639291   .0896575     1.83   0.067    -.0117964    .3396546
   noncomply |  -.3144068   .0963615    -3.26   0.001    -.5032719   -.1255418
      headon |  -.3678404   .1746644    -2.11   0.035    -.7101764   -.0255044
     daytime |  -.4223297   .0824215    -5.12   0.000    -.5838729   -.2607864
     weekday |  -.2729407   .0881706    -3.10   0.002    -.4457519   -.1001295
   sideswipe |  -.2678797   .1526305    -1.76   0.079      -.56703    .0312706
  nrintersec |  -.2329192   .1024442    -2.27   0.023    -.4337061   -.0321323
-------------+----------------------------------------------------------------
daccdttyp    |
  nrintersec |  -.4297652   .0956313    -4.49   0.000     -.617199   -.2423313
     daytime |    .631744   .0954214     6.62   0.000     .4447215    .8187665
   noncomply |  -1.760099    .110548   -15.92   0.000    -1.976769   -1.543429
    othervio |   -1.47428   .3164721    -4.66   0.000    -2.094554   -.8540066
        dens |   .2381624   .1015682     2.34   0.019     .0390924    .4372323
-------------+----------------------------------------------------------------
athrho       |
       _cons |   .0881832   .0786048     1.12   0.262    -.0658793    .2422457
-------------+----------------------------------------------------------------
      /cut11 |   .5014163   .0976875                      .3099524    .6928802
      /cut12 |   1.506739   .1130826                      1.285101    1.728377
      /cut21 |  -1.680589   .1074767                     -1.891239   -1.469938
-------------+----------------------------------------------------------------
         rho |   .0879554   .0779967                     -.0657841    .2376158
------------------------------------------------------------------------------
LR test of indep. eqns. :            chi2(1) =     1.27   Prob > chi2 = 0.2600

The scalars and macros of the estimation are as follows:

Code:

 ereturn list

scalars:
                 e(rc) =  0
                 e(ll) =  -1121.028167390545
          e(converged) =  1
               e(rank) =  16
                  e(k) =  16
               e(k_eq) =  6
               e(k_dv) =  2
                 e(ic) =  2
                  e(N) =  1486
         e(k_eq_model) =  1
               e(df_m) =  7
               e(chi2) =  63.13531987062479
                  e(p) =  3.56044625833e-11
               e(ll_0) =  -1121.662573360585
              e(k_aux) =  3
             e(chi2_c) =  1.268811940080013
                e(p_c) =  .2599896774879261

macros:
            e(chi2_ct) : "LR"
             e(depvar) : "dnmcas daccdttyp"
            e(predict) : "bioprobit_p"
                e(cmd) : "bioprobit"
           e(chi2type) : "Wald"
                e(vce) : "oim"
                e(opt) : "ml"
              e(title) : "Seemingly unrelated bivariate ordered probit regression"
          e(ml_method) : "d2"
               e(user) : "bioprobit_d2"
          e(technique) : "nr"
         e(properties) : "b V"

All help and suggestions would be much appreciated. Thank you.