Stata 15 help for erm_options

[ERM] erm options -- Extended regression model options

Syntax

erm_cmd ... [, extensions options]

erm_cmd is one of eregress, eprobit, eoprobit, or eintreg.

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 ------------------------------------------------------------------------- povariance is available only with eregress and eintreg.

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 nocutsinteract do not interact treatment with cutpoints 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 ------------------------------------------------------------------------- povariance is available only with eregress and eintreg. nocutsinteract is available only with eoprobit.

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 nocutsinteract do not interact treatment with cutpoints nointeract do not interact treatment with covariates in main equation ------------------------------------------------------------------------- povariance is available only with eregress and eintreg. nocutsinteract is available only with eoprobit.

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 -------------------------------------------------------------------------

Description

This entry describes the options that are common to the extended regression commands; see [ERM] eregress, [ERM] eprobit, [ERM] eoprobit, and [ERM] eintreg.

Options

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

endogenous(depvars_en = varlist_en [, enopts]) specifies the model for endogenous covariates. depvars_en is a list of one or more endogenous covariates modeled with varlist_en. This option may be repeated to allow a different model specification for each endogenous covariate. By default, the endogenous covariates are assumed to be continuous, and a linear Gaussian model is used. Unless the nomain suboption is specified, the variables specified in depvars_en are automatically included in the main equation. The following enopts are available:

probit specifies to use a probit model for the endogenous covariates. probit may not be specified with oprobit; however, you may specify endogenous(..., probit) and endogenous(..., oprobit).

oprobit specifies to use an ordered probit model for the endogenous covariates. oprobit may not be specified with probit; however, you may specify endogenous(..., probit) and endogenous(..., oprobit).

povariance specifies that different variance parameters be estimated for each level of the endogenous covariates. In a treatment-effects framework, we refer to levels of endogenous covariates as potential outcomes, and povariance specifies that the variance be estimated separately for each potential outcome. povariance may be specified only with eregress and eintreg and with a binary or an ordinal endogenous covariate.

pocorrelation specifies that different correlation parameters be estimated for each level of the endogenous covariates. In a treatment-effects framework, we refer to levels of endogenous covariates as potential outcomes, and pocorrelation specifies that correlations be estimated separately for each potential outcome. pocorrelation may be specified only with a binary or an ordinal endogenous covariate.

nomain specifies that the endogenous covariate of covariates be excluded from the main model, thus removing the effect. This option is for those who intend to manually construct the effect by adding it to the main model in their own way.

noconstant suppresses the constant term (intercept) in the model for the endogenous covariates.

entreat() and extreat() specify a model for treatment assignment. You may specify only one.

entreat(depvar_tr [= varlist_tr] [, trtopts modopts]) specifies a model for endogenous treatment assignment with depvar_tr = 1 indicating treatment and depvar_tr 0 indicating no treatment. varlist_tr are the covariates for the treatment model; they are optional.

extreat(depvar_tr [, trtopts]) specifies a variable that signals exogenous treatment. depvar_tr = 1 indicates treatment and depvar_tr = 0 indicates no treatment.

trtopts are

povariance specifies that different variance parameters be estimated for each potential outcome (for each treatment level). povariance may be specified only with eregress and eintreg.

pocorrelation specifies that different correlation parameters be estimated for each potential outcome (for each treatment level).

nomain, nocutsinteract, and nointeract affect the way the treatment enters the main equation.

nomain specifies that the main effect of treatment be excluded from the main equation. Thus, a separate intercept is not estimated for each treatment level. In the case of eoprobit, this means separate cutpoints are not added.

nocutsinteract specifies that instead of the default of having separate cutpoints for each treatment level, you get one set of cutpoints that are shifted by a constant value for each treatment level. This is implemented by placing a separate constant in the main equation for each treatment level. nocutsinteract is available only with eoprobit.

nointeract specifies that the treatment variable not be interacted with the other covariates in the main equation.

These options allow you to customize how the treatment enters the main equation. When nomain and nointeract are specified together, they remove the effect entirely, and you will need to explicitly reintroduce the treatment effect.

modopts are

noconstant suppresses the constant term (intercept) in the treatment model.

offset(varname_o) specifies that varname_o be included in the treatment model with the coefficient constrained to 1.

select() and tobitselect() specify a model for endogenous sample selection. You may specify only one.

select(depvar_s = varlist_s [, modopts]) specifies a probit model for sample selection with varlist_s as the covariates for the selection model. When depvar_s = 1, the model's dependent variable is treated as observed (selected); when depvar_s = 0, it is treated as unobserved (not selected).

tobitselect(depvar_s = varlist_s [, ll(varname|#) ul(varname|#) main modopts]) specifies a tobit model for sample selection with depvar_s as a censored selection variable and varlist_s as the covariates for the selection model.

ll(arg) specifies that when depvar_s < arg, the selection variable is treated as censored and the model's dependent variable is unobserved (not selected).

ul(arg) specifies that when depvar_s > arg, the selection variable is treated as censored and the model's dependent variable is unobserved (not selected).

main specifies that the censored selection variable be included as a covariate in the main equation. By default, it is excluded from the main equation.

Only the uncensored values of the selection variable contribute to the likelihood through the main equation. Thus, the selection variable participates as though it were uncensored.

modopts are

noconstant suppresses the constant term (intercept) in the selection model.

offset(varname_o) specifies that varname_o be included in the selection model with the coefficient constrained to 1.

noconstant, offset(varname_o), constraints(numlist), and 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(#) and 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(#) and triintpoints(#) control the number of integration (quadrature) points used to approximate multivariate normal probabilities in the likelihood and scores.

intpoints() sets the number of integration (quadrature) points for integration over four or more dimensions. The number of integration points must be between 3 and 5,000. The default is intpoints(128).

triintpoints() sets the number of integration (quadrature) points for integration over three dimensions. The number of integration points must be between 3 and 5,000. The default is triintpoints(10).

When four dimensions of integration are used in the likelihood, three will be used in the scores. The algorithm for integration over four or more dimensions differs from the algorithm for integration over three dimensions.

+--------------+ ----+ 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 erm_cmd but is not shown in the dialog box:

coeflegend; see [R] estimation options.


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