The name is absent

. eq priv inc yrs

. eq vote inc ptax

. suprob priv vote

Fitting constant only model

Iteration 0: Log Likelihood = -82.529057

Iteration 1: Log Likelihood = -82.078668

Iteration 2: Log Likelihood = -82.076956

Iteration 3: Log Likelihood = -82.076955

Fitting full model

Iteration 0: Log Likelihood = -75.29544

Iteration 1: Log Likelihood = -74.350968

Iteration 2: Log Likelihood = -74.333447

Iteration 3: Log Likelihood = -74.333444

Seemingly unrelated probit regression               Number of obs =      80

Model chi2(4)    =   15.49

Prob > chi2      = 0.0038

Log Likelihood =    -74.3334444                     Pseudo R2        = 0.0943

Example: Robust bivariate probit regression

In this example, we will use the automobile dataset that ships with Stata. We have one binary variable foreign that
denotes whether a car is domestic (foreign = 0) or foreign (foreign = 1). We will also assume for the sake of this example,
that there is another variable guzzler that denotes whether a car is a gas guzzler (guzzler = 1) or not (guzzler = 0). The
guzzler variable was created using gen guzzler = (mpg>=24).

Knowing that most foreign cars imported are smaller and that smaller cars usually get better mileage, we wish to model
these variables with the length and weight of the car.

. biprob foreign guzzler length weight, robust nolog

Number of obs    =      74

Model chi2(4)    =   80.56

Prob > chi2      =  0.0000

Pseudo R2        =  0.4629

I                 Robust

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