Stata 15 help for hetprobit

[R] hetprobit -- Heteroskedastic probit model


hetprobit depvar [indepvars] [if] [in] [weight], het(varlist [, offset(varname_o)]) [options]

options Description ------------------------------------------------------------------------- Model * het(varlist[...]) independent variables to model the variance and possible offset variable noconstant suppress constant term offset(varname) include varname in model with coefficient constrained to 1 asis retain perfect predictor variables 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 waldhet perform Wald test on variance 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 ------------------------------------------------------------------------- * het() is required. The full specification is het(varlist [, offset(varname_o)]). indepvars and varlist may contain factor variables; see fvvarlist. depvar, indepvars, and varlist 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: hetprobit. Weights are not allowed with the bootstrap prefix. vce(), lrmodel, and weights are not allowed with the svy prefix. fweights, iweights, and pweights are allowed; see weight. coeflegend does not appear in the dialog box. See [R] hetprobit postestimation for features available after estimation.


Statistics > Binary outcomes > Heteroskedastic probit regression


hetprobit fits a maximum-likelihood heteroskedastic probit model.

hetprob is a synonym for hetprobit.


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

het(varlist [, offset(varname_o)]) specifies the independent variables and the offset variable, if there is one, in the variance function. het() is required.

offset(varname_o) specifies that selection offset varname_o be included in the model with the coefficient constrained to be 1.

noconstant, offset(varname); see [R] estimation options.

asis forces the retention of perfect predictor variables and their associated perfectly predicted observations and may produce instabilities in maximization; see [R] probit.

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; see [R] estimation options.

waldhet specifies that a Wald test of whether lnsigma2 = 0 be performed instead of the LR test.

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 hetprobit but is not shown in the dialog box:

coeflegend; see [R] estimation options.


Setup . webuse hetprobxmpl

Fit heteroskedastic probit model and use xhet to model the variance . hetprobit y x, het(xhet)

Fit heteroskedastic probit model and request robust standard errors . hetprobit y x, het(xhet) vce(robust)

Stored results

hetprobit stores the following in e():

Scalars e(N) number of observations e(N_f) number of zero outcomes e(N_s) number of nonzero outcomes e(k) number of parameters e(k_eq) number of equations in e(b) 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 e(ll_c) log likelihood, comparison model e(N_clust) number of clusters e(chi2) chi-squared e(chi2_c) chi-squared for heteroskedasticity test e(p_c) p-value for heteroskedasticity test e(df_m_c) degrees of freedom for heteroskedasticity test e(p) p-value for model test 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) hetprobit e(cmdline) command as typed e(depvar) name 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 probit equation e(offset2) offset for variance 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(method) ml 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(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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