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.

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] 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) e(gradient) gradient vector e(V) variance-covariance matrix of the estimators e(V_modelbased) model-based variance

Functions e(sample) marks estimation sample


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