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  • Standard error for the correlation coefficient (rho) in a bivariate probit model

    Hey STATA users,

    I have been having this problem for awhile. It might be more of a theoretical issue. But any help would be appreciated.

    I fit the bivariate probit model:

    biprobit (y1 = x1 x2 x3) (y2 = x1 x2 x3)

    From this we get a correlation coefficient estimate at the very end of the table labelled rho. If I want to obtain a good estimate of the standard error of rho, how could I go about doing this?

    Thank you!

  • #2
    Stein:
    you can go -bootstrap-:
    Code:
    . use http://www.stata-press.com/data/r14/school
    
    . biprobit private vote years logptax loginc
    
    Fitting comparison equation 1:
    
    Iteration 0:   log likelihood = -31.967097 
    Iteration 1:   log likelihood = -31.452424 
    Iteration 2:   log likelihood = -31.448958 
    Iteration 3:   log likelihood = -31.448958 
    
    Fitting comparison equation 2:
    
    Iteration 0:   log likelihood = -63.036914 
    Iteration 1:   log likelihood = -58.534843 
    Iteration 2:   log likelihood = -58.497292 
    Iteration 3:   log likelihood = -58.497288 
    
    Comparison:    log likelihood = -89.946246
    
    Fitting full model:
    
    Iteration 0:   log likelihood = -89.946246 
    Iteration 1:   log likelihood = -89.258897 
    Iteration 2:   log likelihood = -89.254028 
    Iteration 3:   log likelihood = -89.254028 
    
    Bivariate probit regression                     Number of obs     =         95
                                                    Wald chi2(6)      =       9.59
    Log likelihood = -89.254028                     Prob > chi2       =     0.1431
    
    ------------------------------------------------------------------------------
                 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
    -------------+----------------------------------------------------------------
    private      |
           years |  -.0118884   .0256778    -0.46   0.643    -.0622159    .0384391
         logptax |  -.1066962   .6669782    -0.16   0.873    -1.413949    1.200557
          loginc |   .3762037   .5306484     0.71   0.478     -.663848    1.416255
           _cons |  -4.184694   4.837817    -0.86   0.387    -13.66664    5.297253
    -------------+----------------------------------------------------------------
    vote         |
           years |  -.0168561   .0147834    -1.14   0.254    -.0458309    .0121188
         logptax |  -1.288707   .5752266    -2.24   0.025    -2.416131   -.1612839
          loginc |    .998286   .4403565     2.27   0.023     .1352031    1.861369
           _cons |  -.5360573   4.068509    -0.13   0.895    -8.510188    7.438073
    -------------+----------------------------------------------------------------
         /athrho |  -.2764525   .2412099    -1.15   0.252    -.7492153    .1963102
    -------------+----------------------------------------------------------------
             rho |  -.2696186   .2236753                     -.6346806    .1938267
    ------------------------------------------------------------------------------
    LR test of rho=0: chi2(1) = 1.38444                       Prob > chi2 = 0.2393
    
      
    . bootstrap e(rho), reps(200) : biprobit private vote years logptax loginc
    (running biprobit on estimation sample)
    
    Bootstrap replications (200)
    ----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
    ..................................................    50
    ..................................................   100
    ..................................................   150
    ..................................................   200
    
    Bootstrap results                               Number of obs     =         95
                                                    Replications      =        200
    
          command:  biprobit private vote years logptax loginc
            _bs_1:  e(rho)
    
    ------------------------------------------------------------------------------
                 |   Observed   Bootstrap                         Normal-based
                 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
    -------------+----------------------------------------------------------------
           _bs_1 |  -.2696186   .2602223    -1.04   0.300     -.779645    .2404077
    ------------------------------------------------------------------------------
    
    
    
    
    . estat bootstrap, all
    
    Bootstrap results                               Number of obs     =         95
                                                    Replications      =        200
    
          command:  biprobit private vote years logptax loginc
            _bs_1:  e(rho)
    
    ------------------------------------------------------------------------------
                 |    Observed               Bootstrap
                 |       Coef.       Bias    Std. Err.  [95% Conf. Interval]
    -------------+----------------------------------------------------------------
           _bs_1 |  -.26961864   .0369066   .26022232    -.779645   .2404077   (N)
                 |                                      -.6513465   .4084706   (P)
                 |                                      -.6601551   .2706797  (BC)
    ------------------------------------------------------------------------------
    (N)    normal confidence interval
    (P)    percentile confidence interval
    (BC)   bias-corrected confidence interval
    
    .
    Kind regards,
    Carlo
    (Stata 19.0)

    Comment


    • #3
      This is great!

      Thanks a lot Carlo, works perfectly!

      Comment

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