Hello all,

I am using the two Tobit models in the structural equation model (SEM) framework as below,
Array

the code is
Code:
 gsem (Country -> LN_PAT_1, family(gaussian, lcensored(0)) link(identity)) (Country -> RDTA, family(
> gaussian, lcensored(0)) link(identity)) (Industry -> LN_PAT_1, family(gaussian, lcensored(0)) link(
> identity)) (Industry -> RDTA, family(gaussian, lcensored(0)) link(identity)) (year -> LN_PAT_1, fam
> ily(gaussian, lcensored(0)) link(identity)) (year -> RDTA, family(gaussian, lcensored(0)) link(iden
> tity)) (LIQUIDITY -> LN_PAT_1, family(gaussian, lcensored(0)) link(identity)) (LIQUIDITY -> RDTA, f
> amily(gaussian, lcensored(0)) link(identity)) (LN_TA -> LN_PAT_1, family(gaussian, lcensored(0)) li
> nk(identity)) (LN_TA -> RDTA, family(gaussian, lcensored(0)) link(identity)) (PPETA -> LN_PAT_1, fa
> mily(gaussian, lcensored(0)) link(identity)) (PPETA -> RDTA, family(gaussian, lcensored(0)) link(id
> entity)) (LEV -> LN_PAT_1, family(gaussian, lcensored(0)) link(identity)) (LEV -> RDTA, family(gaus
> sian, lcensored(0)) link(identity)), nocapslatent
I got the result as

Code:
Refining starting values:

Grid node 0:   log likelihood = -56980.295

Fitting full model:

Iteration 0:   log likelihood = -56980.295  
Iteration 1:   log likelihood = -50103.258  
Iteration 2:   log likelihood =  -49285.87  
Iteration 3:   log likelihood = -49258.821  
Iteration 4:   log likelihood = -49258.782  
Iteration 5:   log likelihood = -49258.782  

Generalized structural equation model           Number of obs     =     57,490

Response       : LN_PAT_1                       Number of obs     =     57,490
Lower limit    : 0                                 Uncensored     =     34,977
Family         : Gaussian                          Left-censored  =     22,513
Link           : identity                          Right-censored =          0

Response       : RDTA                           Number of obs     =     43,266
Lower limit    : 0                                 Uncensored     =     41,788
Family         : Gaussian                          Left-censored  =      1,478
Link           : identity                          Right-censored =          0

Log likelihood = -49258.782

---------------------------------------------------------------------------------
                |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
LN_PAT_1        |
        Country |   .0089105   .0008246    10.81   0.000     .0072943    .0105267
       Industry |  -.0017527   .0007134    -2.46   0.014    -.0031509   -.0003545
           year |   -.031676   .0016251   -19.49   0.000    -.0348612   -.0284908
      LIQUIDITY |   .0965281    .003857    25.03   0.000     .0889685    .1040877
          LN_TA |   .4295258   .0058899    72.93   0.000     .4179818    .4410698
          PPETA |   -.273905   .0259423   -10.56   0.000     -.324751    -.223059
            LEV |  -1.111073   .0517833   -21.46   0.000    -1.212566    -1.00958
          _cons |   57.96435   3.258487    17.79   0.000     51.57783    64.35086
----------------+----------------------------------------------------------------
RDTA            |
        Country |   .0006957   .0000469    14.84   0.000     .0006038    .0007876
       Industry |   .0005416   .0000365    14.82   0.000       .00047    .0006133
           year |    .000565   .0000829     6.82   0.000     .0004026    .0007274
      LIQUIDITY |   .0022623   .0002236    10.12   0.000      .001824    .0027006
          LN_TA |  -.0172278   .0003179   -54.18   0.000    -.0178509   -.0166046
          PPETA |  -.0318991   .0013397   -23.81   0.000    -.0345248   -.0292733
            LEV |  -.0402615   .0026459   -15.22   0.000    -.0454474   -.0350757
          _cons |  -.8705099   .1662382    -5.24   0.000    -1.196331    -.544689
----------------+----------------------------------------------------------------
 var(e.LN_PAT_1)|   3.577723   .0293979                      3.520566    3.635809
     var(e.RDTA)|   .0079849   .0000555                      .0078768    .0080945
---------------------------------------------------------------------------------
I have two questions here, 1) the first is how to get the marginal effect of these two models?
I got the result like below,

Code:
. mfx compute

Marginal effects after gsem
      y  = Predicted mean (LN_PAT_1) (predict)
         =  .75266612
------------------------------------------------------------------------------
variable |      dy/dx    Std. Err.     z    P>|z|  [    95% C.I.   ]      X
---------+--------------------------------------------------------------------
 Country |   .0050871      .00085    5.97   0.000   .003417  .006758   25.6201
Industry |  -.0048731      .00073   -6.65   0.000   -.00631 -.003436   27.8678
    year |  -.0288715      .00178  -16.23   0.000  -.032357 -.025386    2001.6
LIQUID~Y |   .0672255      .00417   16.11   0.000   .059048  .075403   8.11123
   LN_TA |   .4726395       .0063   74.98   0.000   .460284  .484995   13.0144
   PPETA |  -.3848692      .02974  -12.94   0.000  -.443168 -.326571   .565122
     LEV |   -1.00272      .05403  -18.56   0.000  -1.10862 -.896819   .206329
 CAPEXTA |   2.735847      .21486   12.73   0.000   2.31472  3.15697    .05374
       Q |   .1318931      .00548   24.06   0.000   .121147  .142639   2.00173
------------------------------------------------------------------------------

.

however, I expect to get the result like the below picture, Could you please give me some advice about it?
Array
Please ignore the black area.



Besides, it is really strange that I cannot god of fit by following codes

Code:
. estat gof, stats(all)
estat gof not valid
Do you know how to get the god of fit of these kinds of models (such as RMSEA, CFL TLI)?

Many thanks in advance.