Hello,

I am currently using STATA 15 and I am trying to run a probit model of an indicator variable "Home Bias" (1 if person owns stock in domestic country/0 if not) on three indicator variables for whether an individual is in a specific generation (MG=millenial, GX=gen x, BB=baby boomer) and several controls and survey year controls. I run this model using sampling weights and robust standard errors. (refer to ANALYSIS WEIGHTS for more info on the weights used)

The model does not converge if I either include the sampling weights or, if sampling weights are included, I include all three generational indicators.

Code:
probit HB   MG GX BB age  education white male income_xtile networth_xtile yrx* [pw=wgt] if age<=36 ,  vce(robust)
From reading similar threads, I tried to simplify my model by running the model on simple combinations of the generational covariates that I am interested in and found that the model converges when using each generation indicator separately and in pairs, however including all three is where I run into problems. I also checked that there were sufficient observations in each condition. Below are the tabulated counts for each category...
Home Bias MG GX BB
0 255 414 732
1 1354 2916 983
Total 1609 3330 1715

Additionally from similar threads, I used the -iter()- option and found that BB may be the problematic variable.

Code:
. probit HB   MG GX BB age  education white male income_xtile networth_xtile yrx* [pw=wgt] if age<=36 ,  iter(10) vce(robust) 

note: yrx1 != 0 predicts failure perfectly
      yrx1 dropped and 571 obs not used

note: yrx10 omitted because of collinearity
Iteration 0:   log pseudolikelihood =  -10994329  
Iteration 1:   log pseudolikelihood = -9995004.5  
Iteration 2:   log pseudolikelihood = -9962652.9  
Iteration 3:   log pseudolikelihood = -9962035.8  
Iteration 4:   log pseudolikelihood = -9961948.4  
Iteration 5:   log pseudolikelihood = -9961932.9  
Iteration 6:   log pseudolikelihood = -9961930.8  
Iteration 7:   log pseudolikelihood = -9961930.4  
Iteration 8:   log pseudolikelihood = -9961930.3  
Iteration 9:   log pseudolikelihood = -9961930.3  
Iteration 10:  log pseudolikelihood = -9961930.3  
convergence not achieved

Probit regression                               Number of obs     =      6,103
                                                Wald chi2(14)     =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -9961930.3               Pseudo R2         =     0.0939

--------------------------------------------------------------------------------
               |               Robust
            HB |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
            MG |  -4.980324   .3090391   -16.12   0.000     -5.58603   -4.374619
            GX |  -5.266788   .3430286   -15.35   0.000    -5.939112   -4.594464
            BB |  -5.707422          .        .       .            .           .
           age |   .0179968   .0089211     2.02   0.044     .0005117     .035482
     education |  -.1401491   .0152256    -9.20   0.000    -.1699908   -.1103074
         white |   .2162418   .0593501     3.64   0.000     .0999177    .3325659
          male |  -.3093737   .0855674    -3.62   0.000    -.4770827   -.1416647
  income_xtile |    .023847   .0030531     7.81   0.000      .017863     .029831
networth_xtile |  -.0305111   .0029032   -10.51   0.000    -.0362013    -.024821
          yrx1 |          0  (omitted)
          yrx2 |   1.811163   .1970285     9.19   0.000     1.424994    2.197332
          yrx3 |   1.400022   .1790625     7.82   0.000     1.049066    1.750978
          yrx4 |   1.295315   .1721532     7.52   0.000     .9579008    1.632729
          yrx5 |   1.498705   .1551553     9.66   0.000     1.194606    1.802804
          yrx6 |   1.108657    .147112     7.54   0.000     .8203223    1.396991
          yrx7 |    1.05504   .1383681     7.62   0.000      .783844    1.326237
          yrx8 |   .7401583   .1168842     6.33   0.000     .5110695    .9692471
          yrx9 |    .356723   .1095997     3.25   0.001     .1419115    .5715345
         yrx10 |          0  (omitted)
         _cons |   7.084225   .5015207    14.13   0.000     6.101262    8.067187
--------------------------------------------------------------------------------
Note: 0 failures and 5 successes completely determined.
Warning: convergence not achieved
A potential issue I thought of is that there are no survey years in which there are both millenials and baby boomers who have non-missing values for HB. Below are the counts by year...
year MG GX BB
1989 0 10 561
1992 0 111 563
1995 0 315 361
1998 0 498 230
2001 40 859 0
2004 75 557 0
2007 190 440 0
2010 335 350 0
2013 360 190 0

Would it be possible for someone to assist me in determining why this model will not converge and whether there is a possible solution to get around this issue?

Thanks in advance!