## Stata 15 help for biprobit

```
[R] biprobit -- Bivariate probit regression

Syntax

Bivariate probit regression

biprobit depvar1 depvar2 [indepvars] [if] [in] [weight] [, options]

Seemingly unrelated bivariate probit regression

biprobit equation1 equation2 [if] [in] [weight] [, su_options]

where equation1 and equation2 are specified as

( [eqname: ] depvar [=] [indepvars] [, noconstant offset(varname) ] )

options                       Description
-------------------------------------------------------------------------
Model
noconstant                  suppress constant term
partial                     fit partial observability model
offset1(varname)            offset variable for first equation
offset2(varname)            offset variable for second equation
constraints(constraints)    apply specified linear constraints
collinear                   keep collinear variables

SE/Robust
vce(vcetype)                vcetype may be oim, robust, cluster
clustvar, opg, bootstrap, or jackknife

Reporting
level(#)                    set confidence level; default is level(95)
lrmodel                     perform likelihood-ratio model test instead
of the default Wald test
nocnsreport                 do not display constraints
display_options             control columns and column formats, row
spacing, line width, display of omitted
variables and base and empty cells, and
factor-variable labeling

Maximization
maximize_options            control the maximization process; seldom
used

coeflegend                  display legend instead of statistics
-------------------------------------------------------------------------

su_options                    Description
-------------------------------------------------------------------------
Model
partial                     fit partial observability model
constraints(constraints)    apply specified linear constraints
collinear                   keep collinear variables

SE/Robust
vce(vcetype)                vcetype may be oim, robust, cluster
clustvar, opg, bootstrap, or jackknife

Reporting
level(#)                    set confidence level; default is level(95)
lrmodel                     perform the likelihood-ratio model test
instead of the default Wald test
nocnsreport                 do not display constraints
display_options             control columns and column formats, row
spacing, line width, display of omitted
variables and base and empty cells, and
factor-variable labeling

Maximization
maximize_options            control the maximization process; seldom
used

coeflegend                  display legend instead of statistics
-------------------------------------------------------------------------

indepvars may contain factor variables; see fvvarlist.
depvar1, depvar2, indepvars, and depvar may contain time-series
operators; see tsvarlist.
bayes, bootstrap, by, fp, jackknife, rolling, statsby, and svy are
allowed; see prefix.  For more details, see [BAYES] bayes: biprobit.
Weights are not allowed with the bootstrap prefix.
vce(), lrmodel, and weights are not allowed with the svy prefix.
pweights, fweights, and iweights are allowed; see weight.
coeflegend does not appear in the dialog box.
See [R] biprobit postestimation for features available after estimation.

biprobit

Statistics > Binary outcomes > Bivariate probit regression

seemingly unrelated biprobit

Statistics > Binary outcomes > Seemingly unrelated bivariate probit
regression

Description

biprobit fits maximum-likelihood two-equation probit models -- either a
bivariate probit or a seemingly unrelated probit (limited to two
equations).

Options

+-------+
----+ Model +------------------------------------------------------------

noconstant; see [R] estimation options.

partial specifies that the partial observability model be fit.  This
particular model commonly has poor convergence properties, so we
recommend that you use the difficult option if you want to fit the
Poirier partial observability model; see [R] maximize.

This model computes the product of the two dependent variables so
that you do not have to replace each with the product.

offset1(varname), offset2(varname), constraints(constraints), collinear;
see [R] estimation options.

+-----------+
----+ SE/Robust +--------------------------------------------------------

vce(vcetype) specifies the type of standard error reported, which
includes types that are derived from asymptotic theory (oim, opg),
that are robust to some kinds of misspecification (robust), that
allow for intragroup correlation (cluster clustvar), and that use
bootstrap or jackknife methods (bootstrap, jackknife); see [R]
vce_option.

+-----------+
----+ Reporting +--------------------------------------------------------

level(#), lrmodel, nocnsreport; see [R] estimation options.

display_options:  noci, nopvalues, noomitted, vsquish, noemptycells,
baselevels, allbaselevels, nofvlabel, fvwrap(#), fvwrapon(style),
cformat(%fmt), pformat(%fmt), sformat(%fmt), and nolstretch; see [R]
estimation options.

+--------------+
----+ Maximization +-----------------------------------------------------

maximize_options: difficult, technique(algorithm_spec), iterate(#),
[no]log, trace, gradient, showstep, hessian, showtolerance,
tolerance(#), ltolerance(#), nrtolerance(#), nonrtolerance, and
from(init_specs); see [R] maximize.  These options are seldom used.

Setting the optimization type to technique(bhhh) resets the default
vcetype to vce(opg).

The following option is available with biprobit but is not shown in the
dialog box:

coeflegend; see [R] estimation options.

Examples

Setup
. webuse school

Bivariate probit regression
. biprobit private vote logptax loginc years

Seemingly unrelated bivariate probit regression
. biprobit (private = logptax loginc years) (vote = logptax years)

Seemingly unrelated bivariate probit regression with robust standard
errors
. biprobit (private = logptax loginc years) (vote = logptax years),
vce(robust)

Stored results

biprobit stores the following in e():

Scalars
e(N)                     number of observations
e(k)                     number of parameters
e(k_eq)                  number of equations in e(b)
e(k_aux)                 number of auxiliary parameters
e(k_eq_model)            number of equations in overall model test
e(k_dv)                  number of dependent variables
e(df_m)                  model degrees of freedom
e(ll)                    log likelihood
e(ll_0)                  log likelihood, constant-only model (lrmodel
only)
e(ll_c)                  log likelihood, comparison model
e(N_clust)               number of clusters
e(chi2)                  chi-squared
e(chi2_c)                chi-squared for comparison test
e(p)                     p-value for model test
e(rho)                   rho
e(rank)                  rank of e(V)
e(rank0)                 rank of e(V) for constant-only model
e(ic)                    number of iterations
e(rc)                    return code
e(converged)             1 if converged, 0 otherwise

Macros
e(cmd)                   biprobit
e(cmdline)               command as typed
e(depvar)                names of dependent variables
e(wtype)                 weight type
e(wexp)                  weight expression
e(title)                 title in estimation output
e(clustvar)              name of cluster variable
e(offset1)               offset for first equation
e(offset2)               offset for second equation
e(chi2type)              Wald or LR; type of model chi-squared test
e(chi2_ct)               Wald or LR; type of model chi-squared test
corresponding to e(chi2_c)
e(vce)                   vcetype specified in vce()
e(vcetype)               title used to label Std. Err.
e(opt)                   type of optimization
e(which)                 max or min; whether optimizer is to perform
maximization or minimization
e(ml_method)             type of ml method
e(user)                  name of likelihood-evaluator program
e(technique)             maximization technique
e(properties)            b V
e(predict)               program used to implement predict
e(marginsok)             predictions allowed by margins
e(marginsnotok)          predictions disallowed by margins
e(asbalanced)            factor variables fvset as asbalanced
e(asobserved)            factor variables fvset as asobserved

Matrices
e(b)                     coefficient vector
e(Cns)                   constraints matrix
e(ilog)                  iteration log (up to 20 iterations)