Stata 15 help for heckoprobit

[R] heckoprobit -- Ordered probit model with sample selection

Syntax

heckoprobit 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 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 noheader do not display header above parameter table nofootnote do not display footnotes below parameter table 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, jackknife, rolling, statsby, and svy are allowed; see prefix. For more details, see [BAYES] bayes: heckoprobit. Weights are not allowed with the bootstrap prefix. vce(), first, 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] heckoprobit postestimation for features available after estimation.

Menu

Statistics > Sample-selection models > Ordered probit model with selection

Description

heckoprobit fits maximum-likelihood ordered probit models with sample selection.

Options

+-------+ ----+ 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_s 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.

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.

noheader suppresses the header above the parameter table, the display that reports the final log-likelihood value, number of observations, etc.

nofootnote suppresses the footnotes displayed below the parameter table.

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 heckoprobit but is not shown in the dialog box:

coeflegend; see [R] estimation options.

Example

Setup . webuse womensat

Fit an ordered probit model with sample selection based on employment . heckoprobit satisfaction educ age, select(work=educ age i.married##c.children)

Stored results

heckoprobit 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(N_cd) number of completely determined observations e(k_cat) number of categories 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_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(ic) number of iterations e(rc) return code e(converged) 1 if converged, 0 otherwise

Macros e(cmd) heckoprobit 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(marginsok) predictions allowed by margins e(marginsnotok) predictions disallowed by margins e(marginsdefault) default predict() specification for 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(cat) category values e(V) variance-covariance matrix of the estimators e(V_modelbased) model-based variance

Functions e(sample) marks estimation sample


© Copyright 1996–2018 StataCorp LLC   |   Terms of use   |   Privacy   |   Contact us   |   What's new   |   Site index