Stata 15 help for eintreg

[ERM] eintreg -- Extended interval regression

Syntax

Basic interval regression with endogenous covariates

eintreg depvar_1 depvar_2 [indepvars], endogenous(depvars_en = varlist_en) [options]

Basic interval regress with endogenous treatment assignment

eintreg depvar_1 depvar_2 [indepvars], entreat(depvar_tr [= varlist_tr]) [options]

Basic interval regression with exogenous treatment assignment

eintreg depvar_1 depvar_2 [indepvars], extreat(tvar) [options]

Basic interval regression with sample selection

eintreg depvar_1 depvar_2 [indepvars], select(depvar_s = varlist_s) [options]

Basic interval regression with tobit sample selection

eintreg depvar_1 depvar_2 [indepvars], tobitselect(depvar_s = varlist_s) [options]

Interval regression combining endogenous covariates, treatment, and selection

eintreg depvar_1 depvar_2 [indepvars] [if] [in] [weight] [, extensions options]

depvar_1 and depvar_2 should have the following form:

Type of data depvar1 depvar2 ---------------------------------------------- point data a = [a,a] a a interval data [a,b] a b left-censored data (-inf,b] . b right-censored data [a,inf) a .

missing . . ----------------------------------------------

extensions Description ------------------------------------------------------------------------- Model endogenous(enspec) model for endogenous covariates; may be repeated entreat(entrspec) model for endogenous treatment assignment extreat(extrspec) exogenous treatment select(selspec) probit model for selection tobitselect(tselspec) tobit model for selection -------------------------------------------------------------------------

options Description ------------------------------------------------------------------------- Model noconstant suppress constant term offset(varname_o) include varname_o in model with coefficient constrained to 1 constraints(numlist) 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) 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

Integration intpoints(#) set the number of integration (quadrature) points for integration over four or more dimensions; default is intpoints(128) triintpoints(#) set the number of integration (quadrature) points for integration over three dimensions; default is triintpoints(10)

Maximization maximize_options control the maximization process; seldom used

coeflegend display legend instead of statistics -------------------------------------------------------------------------

enspec is depvars_en = varlist_en [, enopts]

where depvars_en is a list of endogenous covariates. Each variable in depvars_en specifies an endogenous covariate model using the common varlist_en and options.

entrspec is depvar_tr [= varlist_tr] [, entropts]

where depvar_tr is a variable indicating treatment assignment. varlist_tr is a list of covariates predicting treatment assignment.

extrspec is tvar [, extropts]

where tvar is a variable indicating treatment assignment.

selspec is depvar_s = varlist_s [, noconstant offset(varname_o)]

where depvar_s is a variable indicating selection status. depvar_s must be coded as 0, indicating that the observation was not selected, or 1, indicating that the observation was selected. varlist_s is a list of covariates predicting selection.

tselspec is depvar_s = varlist_s [, tselopts]

where depvar_s is a continuous variable. varlist_s is a list of covariates predicting depvar_s. The censoring status of depvar_s indicates selection, where a censored depvar_s indicates that the observation was not selected and a noncensored depvar_s indicates that the observation was selected.

enopts Description ------------------------------------------------------------------------- Model probit treat endogenous covariate as binary oprobit treat endogenous covariate as ordinal povariance estimate a different variance for each level of a binary or an ordinal endogenous covariate pocorrelation estimate different correlations for each level of a binary or an ordinal endogenous covariate nomain do not add endogenous covariate to main equation noconstant suppress constant term -------------------------------------------------------------------------

entropts Description ------------------------------------------------------------------------- Model povariance estimate a different variance for each potential outcome pocorrelation estimate different correlations for each potential outcome nomain do not add treatment indicator to main equation nointeract do not interact treatment with covariates in main equation noconstant suppress constant term offset(varname_o) include varname_o in model with coefficient constrained to 1 -------------------------------------------------------------------------

extropts Description ------------------------------------------------------------------------- Model povariance estimate a different variance for each potential outcome pocorrelation estimate different correlations for each potential outcome nomain do not add treatment indicator to main equation nointeract do not interact treatment with covariates in main equation -------------------------------------------------------------------------

tselopts Description ------------------------------------------------------------------------- Model ll(varname|#) left-censoring variable or limit ul(varname|#) right-censoring variable or limit main add selection indicator to main equation noconstant suppress constant term offset(varname_o) include varname_o in model with coefficient constrained to 1 -------------------------------------------------------------------------

indepvars, varlist_en, varlist_tr, and varlist_s may contain factor variables; see fvvarlist. depvar_1, depvar_2, indepvars, depvars_en, varlist_en, depvar_tr, varlist_tr, tvar, 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() and weights are not allowed with the svy prefix. fweights, iweights, and pweights are allowed; see weight. coeflegend does not appear in the dialog box. See [ERM] eintreg postestimation for features available after estimation.

Menu

Statistics > Endogenous covariates > Models adding selection and treatment > Interval regression

Description

eintreg fits an interval regression model that accommodates any combination of endogenous covariates, nonrandom treatment assignment, and endogenous sample selection. Continuous, binary, and ordinal endogenous covariates are allowed. Treatment assignment may be endogenous or exogenous. A probit or tobit model may be used to account for endogenous sample selection.

Options

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

endogenous(enspec), entreat(entrspec), extreat(extrspec), select(selspec), tobitselect(tselspec); see [ERM] erm options.

noconstant, offset(varname_o), constraints(numlist), collinear; see [R] estimation options.

+-----------+ ----+ SE/Robust +--------------------------------------------------------

vce(vcetype); see [ERM] erm options.

+-----------+ ----+ Reporting +--------------------------------------------------------

level(#), 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.

+-------------+ ----+ Integration +------------------------------------------------------

intpoints(#), triintpoints(#); see [ERM] erm 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.

Setting the optimization type to technique(bhhh) resets the default vcetype to vce(opg).

The following option is available with eintreg but is not shown in the dialog box:

coeflegend; see [R] estimation options.

Examples

Setup . webuse class10

Interval regression with endogenous covariate hsgpa . eintreg gpal gpau income, endogenous(hsgpa = income i.hscomp)

As above, and account for endogenous sample selection . eintreg gpal gpau income, endogenous(hsgpa = income i.hscomp) select(graduate = hsgpa income i.roommate i.program)

Stored results

eintreg 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_unc) number of uncensored observations e(N_lc) number of left-censored observations e(N_rc) number of right-censored observations e(N_int) number of interval-censored observations e(k) number of parameters e(k_cat#) number of categories for the #th depvar, ordinal e(k_eq) number of equations in e(b) e(k_eq_model) number of equations in overall model test e(k_dv) number of dependent variables e(k_aux) number of auxiliary parameters e(df_m) model degrees of freedom e(ll) log likelihood e(N_clust) number of clusters e(chi2) chi-squared e(p) p-value for model test e(n_quad) number of integration points for multivariate normal e(n_quad3) number of integration points for trivariate normal 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) eintreg 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(offset#) offset for the #th depvar, where # is determined by equation order in output e(chi2type) Wald; type of model 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(estat_cmd) program used to implement estat e(predict) program used to implement predict e(marginsok) predictions allowed by margins e(marginsnotok) predictions disallowed by margins e(asbalanced) factor variables fvset as asbalanced e(asobserved) factor variables fvset as asobserved

Matrices e(b) coefficient vector e(cat#) categories for the #th depvar, ordinal 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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