Stata 15 help for heckprobit

[R] heckprobit -- Probit model with sample selection


heckprobit depvar indepvars [if] [in] [weight] , select([depvar_s =] varlist_s [, noconstant offset(varname_o)]) [options]

options Description ------------------------------------------------------------------------- Model * select() specify selection equation: dependent and independent variables; whether to have constant term and offset variable noconstant suppress constant term offset(varname) include varname in model with coefficient constrained to 1 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) first report first-step probit estimates 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 ------------------------------------------------------------------------- * select() is required. The full specification is select([depvar_s =] varlist_s [, noconstant offset(varname_o)]). indepvars and varlist_s may contain factor variables; see fvvarlist. depvar, indepvars, depvar_s, and varlist_s 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: heckprobit. Weights are not allowed with the bootstrap prefix. vce(), first, 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] heckprobit postestimation for features available after estimation.


Statistics > Sample-selection models > Probit model with sample selection


heckprobit fits maximum-likelihood probit models with sample selection.

heckprob is a synonym for heckprobit.


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

select([depvar_s =] varlist_s [, noconstant offset(varname_o)]) specifies the variables and options for the selection equation. It is an integral part of specifying a selection model and is required. The selection equation should contain at least one variable that is not in the outcome equation.

If depvar_s is specified, it should be coded as 0 or 1, 0 indicating an observation not selected and 1 indicating a selected observation. If depvar_s is not specified, observations for which depvar is not missing are assumed selected, and those for which depvar is missing are assumed not selected.

noconstant suppresses the selection constant term (intercept).

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

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

first specifies that the first-step probit estimates of the selection equation be displayed before estimation.

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 heckprobit but is not shown in the dialog box:

coeflegend; see [R] estimation options.


Setup . webuse school

Fit a probit model with sample selection . heckprobit private years logptax, sel(vote=years loginc logptax)

Replay results, but display legend of coefficients rather than the statistics for the coefficients . heckprobit, coeflegend

Stored results

heckprobit stores the following in e():

Scalars e(N) number of observations e(N_selected) number of selected observations e(N_nonselected) number of nonselected observations 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_aux) number of auxiliary parameters 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 comparison test e(p) p-value for model test e(p_c) p-value for comparison 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) heckprobit 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 regression equation e(offset2) offset for selection equation e(chi2type) Wald or LR; type of model chi-squared test e(chi2_ct) type of comparison chi-squared test 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(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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