help hetprob dialogs: hetprob svy: hetprob
also see: hetprob postestimation
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Title
[R] hetprob -- Heteroskedastic probit model
Syntax
hetprob depvar [indepvars] [if] [in] [weight] , het(varlist [,
offset(varname)]) [options]
options description
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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)
noskip perform likelihood-ratio test
nolrtest perform Wald test on variance
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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* het() is required. The full specification is
het(varlist [, offset(varname)])
+ coeflegend does not appear in the dialog box.
indepvars and varlist may contain factor variables; see fvvarlist.
depvar, indepvars, and varlist 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(), noskip, and weights are not allowed with the svy prefix.
fweights, iweights, and pweights are allowed; see weight.
See [R] hetprob postestimation for features available after estimation.
Menu
Statistics > Binary outcomes > Heteroskedastic probit regression
Description
hetprob fits a maximum-likelihood heteroskedastic probit model.
See logistic estimation commands for a list of related estimation
commands.
Options
+-------+
----+ Model +------------------------------------------------------------
het(varlist [, offset(varname)]) specifies the independent variables and
the offset variable, if there is one, in the variance function.
het() is required.
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, 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.
noskip requests fitting of the constant-only model and calculation of the
corresponding likelihood-ratio chi-squared statistic for testing
significance of the full model. By default, a Wald chi-squared
statistic is computed for testing the significance of the full model.
nolrtest specifies that a Wald test of whether lnsigma2 = 0 be performed
instead of the LR test.
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 hetprob but is not shown in the
dialog box:
coeflegend; see [R] estimation options.
Example
. webuse hetprobxmpl
. hetprob y x, het(xhet)
. hetprob y x, het(xhet) vce(robust)
Saved results
hetprob saves 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
e(k_eq_model) number of equations in model Wald test
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 heteroskedasticity LR test
e(p_c) p-value for heteroskedasticity LR test
e(df_m_c) degrees of freedom for heteroskedasticity LR test
e(p) significance
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) hetprob
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) requested estimation method
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
Functions
e(sample) marks estimation sample
Also see
Manual: [R] hetprob
Help: [R] hetprob postestimation;
[R] logistic, [R] probit, [SVY] svy estimation, [XT] xtprobit
