help biprobit dialogs: biprobit
seemingly unrelated biprobit
svy: biprobit
svy: seemingly unrelated biprobit
also see: biprobit postestimation
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Title
[R] biprobit -- Bivariate probit regression
Syntax
Bivariate probit model
biprobit depvar1 depvar2 [varlist] [if] [in] [weight] [, options]
Seemingly unrelated bivariate probit model
biprobit equation1 equation2 [if] [in] [weight] [, su_options]
where equation1 and equation2 are specified as
( [eqname: ] depvar [=] [varlist] [, noconstant offset(varname) ] )
options description
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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)
noskip perform likelihood-ratio test
nocnsreport do not display constraints
display_options control spacing and display of omitted
variables and base and empty cells
Maximization
maximize_options control the maximization process; seldom
used
+ coeflegend display coefficients' legend instead of
coefficient table
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su_options description
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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)
noskip perform likelihood-ratio test
nocnsreport do not display constraints
display_options control spacing and display of omitted
variables and base and empty cells
Maximization
maximize_options control the maximization process; seldom
used
+ coeflegend display coefficients' legend instead of
coefficient table
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+ coeflegend does not appear in the dialog box.
indepvars may contain factor variables; see fvvarlist.
depvar1, depvar2, varlist, and depvar may contain time-series operators;
see tsvarlist.
bootstrap, by, jackknife, rolling, statsby, and svy are allowed; see
prefix.
Weights are not allowed with the bootstrap prefix.
vce(), noskip, and weights are not allowed with the svy prefix.
pweights, fweights, and iweights are allowed; see weight.
See [R] biprobit postestimation for features available after estimation.
Menu
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] ml.
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, that are
robust to some kinds of misspecification, that allow for intragroup
correlation, and that use bootstrap or jackknife methods; see [R]
vce_option.
+-----------+
----+ Reporting +--------------------------------------------------------
level(#); see [R] estimation options.
noskip specifies that a full maximum-likelihood model with only a
constant for the regression equation be fit. This model is not
displayed but is used as the base model to compute a likelihood-ratio
test for the model test statistic displayed in the estimation header.
By default, the overall model test statistic is an asymptotically
equivalent Wald test of all the parameters in the regression equation
being zero (except the constant). For many models, this option can
substantially increase estimation time.
nocnsreport; see [R] estimation options.
display_options: noomitted, vsquish, noemptycells, baselevels,
allbaselevels; see [R] estimation options.
+--------------+
----+ Maximization +-----------------------------------------------------
maximize_options: difficult, technique(algorithm_spec), iterate(#),
[no]log, trace, gradient, showstep, hessian, showtolerance,
tolerance(#), ltolerance(#), nrtolerance(#), nonrtolerance,
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 model
. biprobit private vote logptax loginc years
Seemingly unrelated bivariate probit model
. biprobit (private = logptax loginc years) (vote = logptax years)
Seemingly unrelated bivariate probit model with robust standard errors
. biprobit (private = logptax loginc years) (vote = logptax years),
vce(robust)
Saved results
biprobit saves the following in e():
Scalars
e(N) number of observations
e(k) number of parameters
e(k_eq) number of equations
e(k_aux) number of auxiliary parameters
e(k_eq_model) number of equations in model Wald test
e(k_dv) number of dependent variables
e(k_autoCns) number of base, empty, and omitted constraints
e(df_m) model degrees of freedom
e(ll) log likelihood
e(ll_0) log likelihood, constant-only model (noskip
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) significance
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 variable
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(diparm#) display transformed parameter #
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(singularHmethod) m-marquardt or hybrid; method used when
Hessian is singular
e(crittype) optimization criterion
e(properties) b V
e(predict) program used to implement predict
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)
e(gradient) gradient vector
e(V) variance-covariance matrix of the estimators
e(V_modelbased) model-based variance
Functions
e(sample) marks estimation sample
Also see
Manual: [R] biprobit
Help: [R] biprobit postestimation;
[R] mprobit, [R] probit, [SVY] svy estimation
