Stata 15 help for tobit

[R] tobit -- Tobit regression

Syntax

tobit depvar [indepvars] [if] [in] [weight] [, options]

options Description ------------------------------------------------------------------------- Model noconstant suppress constant term ll[(varname|#)] left-censoring variable or limit ul[(varname|#)] right-censoring variable or limit 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, 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

Maximization maximize_options control the maximization process; seldom used

coeflegend display legend instead of statistics ------------------------------------------------------------------------- indepvars may contain factor variables; see fvvarlist. depvar and indepvars may contain time-series operators; see tsvarlist. bayes, bootstrap, by, fmm, fp, jackknife, nestreg, rolling, statsby, stepwise, and svy are allowed; see prefix. For more details, see [BAYES] bayes: tobit and [FMM] fmm: tobit. Weights are not allowed with the bootstrap prefix. aweights are not allowed with the jackknife prefix. vce() and weights are not allowed with the svy prefix. aweights, fweights, iweights, and pweights are allowed; see weight. coeflegend does not appear in the dialog box. See [R] tobit postestimation for features available after estimation.

Menu

Statistics > Linear models and related > Censored regression > Tobit regression

Description

tobit fits models for continuous responses where the outcome variable is censored. Censoring limits may be fixed for all observations or vary across observations.

Options

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

noconstant; see [R] estimation options.

ll[(varname|#)] and ul[(varname|#)] indicate the lower and upper limits for censoring, respectively. Observations with depvar < ll() are left-censored; observations with depvar > ul() are right-censored; and remaining observations are not censored. You do not have to specify the censoring value. If you specify ll, the lower limit is the minimum of depvar. If you specify ul, the upper limit is the maximum of depvar.

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), 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(#), 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.

+--------------+ ----+ 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. These options are seldom used.

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

coeflegend; see [R] estimation options.

Examples

--------------------------------------------------------------------------- Setup . sysuse auto . generate wgt=weight/1000

Censored from below . tobit mpg wgt, ll(17)

--------------------------------------------------------------------------- Setup . sysuse auto, clear . generate wgt=weight/100

Censored from above . tobit mpg wgt, ul(24)

Clustered on foreign . tobit mpg wgt, ul(24) vce(cluster foreign)

Two-limit tobit . tobit mpg wgt, ll(17) ul(24)

--------------------------------------------------------------------------- Setup . webuse gpa, clear

Censored at the minimum of gpa2 . tobit gpa2 hsgpa pincome program, ll

--------------------------------------------------------------------------- Setup . webuse mroz87

Censored from below at zero . tobit whrs75 nwinc wedyrs wexper c.wexper#c.wexper wifeage kl6 k618, ll(0)

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

Stored results

tobit stores the following in e():

Scalars e(N) number of 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(k) number of parameters e(k_eq) number of equations in e(b) e(k_aux) number of auxiliary parameters e(k_dv) number of dependent variables e(df_m) model degrees of freedom e(df_r) residual degrees of freedom e(r2_p) pseudo-R-squared e(ll) log likelihood e(ll_0) log likelihood, constant-only model e(N_clust) number of clusters e(chi2) chi-squared e(F) F statistic e(p) p-value for model test 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) tobit e(cmdline) command as typed e(depvar) name of dependent variable e(llopt) minimum of depvar or contents of ll() e(ulopt) maximum of depvar or contents of ul() e(wtype) weight type e(wexp) weight expression e(covariates) list of covariates e(title) title in estimation output e(clustvar) name of cluster variable e(offset) linear offset variable e(chi2type) 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(method) estimation method: ml 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(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


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