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

__Syntax__

**heckoprobit** *depvar* *indepvars* [*if*] [*in*] [*weight*]**,** __sel__**ect(**[*depvar_s* **=**]
*varlist_s* [**,** __nocons__**tant** __off__**set(***varname_o***)**]**)** [*options*]

*options* Description
-------------------------------------------------------------------------
Model
* __sel__**ect()** specify selection equation: dependent and
independent variables; whether to have
constant term and offset variable
__off__**set(***varname***)** include *varname* in model with coefficient
constrained to 1
__const__**raints(***constraints***)** apply specified linear constraints
__col__**linear** keep collinear variables

SE/Robust
**vce(***vcetype***)** *vcetype* may be **oim**, __r__**obust**, __cl__**uster** *clustvar*,
**opg**, __boot__**strap**, or __jack__**knife**

Reporting
__l__**evel(***#***)** set confidence level; default is **level(95)**
__fir__**st** report first-step probit estimates
__nohead__**er** do not display header above parameter table
__nofoot__**note** do not display footnotes below parameter
table
__nocnsr__**eport** 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

__coefl__**egend** display legend instead of statistics
-------------------------------------------------------------------------
* **select()** is required. The full specification is
__sel__**ect(**[*depvar_s* **=**] *varlist_s* [**,** __nocons__**tant** __off__**set(***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.
**pweight**s, **fweight**s, and **iweight**s 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**, __nopv__**alues**, __noomit__**ted**, **vsquish**, __noempty__**cells**,
__base__**levels**, __allbase__**levels**, __nofvlab__**el**, **fvwrap(***#***)**, **fvwrapon(***style***)**,
**cformat(***%fmt***)**, **pformat(%***fmt***)**, **sformat(%***fmt***)**, and **nolstretch**; see **[R]**
**estimation options**.

+--------------+
----+ Maximization +-----------------------------------------------------

*maximize_options*: __dif__**ficult**, __tech__**nique(***algorithm_spec***)**, __iter__**ate(***#***)**,
[__no__]__lo__**g**, __tr__**ace**, __grad__**ient**, **showstep**, __hess__**ian**, __showtol__**erance**,
__tol__**erance(***#***)**, __ltol__**erance(***#***)**, __nrtol__**erance(***#***)**, __nonrtol__**erance**, 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