help heckprob dialogs: heckprob svy: heckprob
also see: heckprob postestimation
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Title
[R] heckprob -- Probit model with sample selection
Syntax
heckprob depvar indepvars [if] [in] [weight] , select([depvar_s =]
varlist_s [, offset(varname) noconstant]) [options]
options description
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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
noskip perform likelihood-ratio test
nocnsreport do not display constraints
display_options control spacing and display of omitted
variables and base and empty cells
Maximization
maximize_options control the maximization process; seldom
used
+ coeflegend display coefficients' legend instead of
coefficient table
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* select() is required. The full specification is
select([depvar_s =] varlist_s [, offset(varname) noconstant])
+ coeflegend does not appear in the dialog box.
indepvars and varlist_s may contain factor variables; see fvvarlist.
depvar, indepvars, depvar_s, and varlist_s may contain time-series
operators; see tsvarlist.
bootstrap, by, jackknife, rolling, statsby, and svy are allowed; see
prefix.
Weights are not allowed with the bootstrap prefix.
vce(), first, noskip, and weights are not allowed with the svy prefix.
pweights, fweights, and iweights are allowed; see weight.
See [R] heckprob postestimation for features available after estimation.
Menu
Statistics > Sample-selection models > Probit model with selection
Description
heckprob fits maximum-likelihood probit models with sample selection.
Options
+-------+
----+ Model +------------------------------------------------------------
select(...) 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, 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, that are
robust to some kinds of misspecification, that allow for intragroup
correlation, and that use bootstrap or jackknife methods; 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.
noskip specifies that a full maximum-likelihood model with only a
constant for the regression equation be fit. This model is not
displayed but is used as the base model to compute a likelihood-ratio
test for the model test statistic displayed in the estimation header.
By default, the overall model test statistic is an asymptotically
equivalent Wald test that all the parameters in the regression
equation are zero (except the constant). For many models, this
option can substantially increase estimation time.
nocnsreport; see [R] estimation options.
display_options: noomitted, vsquish, noemptycells, baselevels,
allbaselevels; see [R] estimation options.
+--------------+
----+ Maximization +-----------------------------------------------------
maximize_options: difficult, technique(algorithm_spec), iterate(#),
[no]log, trace, gradient, showstep, hessian, showtolerance,
tolerance(#), ltolerance(#), nrtolerance(#), nonrtolerance,
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 heckprob but is not shown in the
dialog box:
coeflegend; see [R] estimation options.
Example
Setup
. webuse school
Fit a probit model with sample selection
. heckprob private years logptax, sel(vote=years loginc logptax)
Saved results
heckprob saves the following in e():
Scalars
e(N) number of observations
e(N_cens) number of censored observations
e(k) number of parameters
e(k_eq) number of equations
e(k_eq_model) number of equations in model Wald test
e(k_aux) number of auxiliary parameters
e(k_dv) number of dependent variables
e(k_autoCns) number of base, empty, and omitted constraints
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_c) p-value for comparison test
e(p) significance of 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) heckprob
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(diparm#) display transformed parameter #
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(singularHmethod) m-marquardt or hybrid; method used when Hessian is
singular
e(crittype) optimization criterion
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
e(ml_h) derivative tolerance, (abs(b)+1e-3)*1e-3
e(ml_scale) derivative scale factor
Functions
e(sample) marks estimation sample
Also see
Manual: [R] heckprob
Help: [R] heckprob postestimation;
[R] heckman, [R] probit, [R] treatreg, [SVY] svy estimation
