Stata 15 help for truncreg

[R] truncreg -- Truncated regression

Syntax

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

options Description ------------------------------------------------------------------------- Model noconstant suppress constant term ll(varname|#) left-truncation variable or limit ul(varname|#) right-truncation 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, opg, bootstrap, or jackknife

Reporting level(#) set confidence level; default is level(95) lrmodel perform the likelihood-ratio model test instead of the default Wald test 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 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, mi estimate, rolling, statsby, and svy are allowed; see prefix. For more details, see [BAYES] bayes: truncreg and [FMM] fmm: truncreg. vce(bootstrap) and vce(jackknife) are not allowed with the mi estimate prefix. Weights are not allowed with the bootstrap prefix. aweights are not allowed with the jackknife prefix. vce(), lrmodel, 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] truncreg postestimation for features available after estimation.

Menu

Statistics > Linear models and related > Truncated regression

Description

truncreg fits a regression model of depvar on indepvars from a sample drawn from a restricted part of the population. Under the normality assumption for the whole population, the error terms in the truncated regression model have a truncated normal distribution, which is a normal distribution that has been scaled upward so that the distribution integrates to one over the restricted range.

Options

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

noconstant; see [R] estimation options.

ll(varname|#) and ul(varname|#) indicate the lower and upper limits for truncation, respectively. You may specify one or both. Observations with depvar < ll() are left-truncated, observations with depvar > ul() are right-truncated, and the remaining observations are not truncated. See [R] tobit for a more detailed description.

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, 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(#), lrmodel, 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, but you may use the ltol(#) option to relax the convergence criterion; the default is 1e-6 during specification searches.

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

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

coeflegend; see [R] estimation options.

Examples

--------------------------------------------------------------------------- Setup . webuse laborsub . regress whrs kl6 k618 wa we if whrs > 0

Truncated regression with truncation from below 0 . truncreg whrs kl6 k618 wa we, ll(0)

--------------------------------------------------------------------------- Setup . sysuse auto . generate lowmpg = 20 if foreign == 0 . replace lowmpg = 25 if foreign == 1

Truncated regression with lowmpg containing the limit for truncation . truncreg mpg price length displacement, ll(lowmpg) ---------------------------------------------------------------------------

Stored results

truncreg stores the following in e():

Scalars e(N) number of observations e(N_bf) number of observations before truncation e(chi2) model chi-squared e(k_eq) number of equations in e(b) e(k_eq_model) number of equations in overall model test e(k_aux) number of auxiliary parameters e(df_m) model degrees of freedom e(ll) log likelihood e(ll_0) log likelihood, constant-only model e(N_clust) number of clusters e(sigma) estimate of sigma 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

Scalars e(cmd) truncreg e(cmdline) command as typed e(llopt) contents of ll(), if specified e(ulopt) contents of ul(), if specified 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 e(chi2type) Wald or LR; 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(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 e(means) means of independent variables e(dummy) indicator for dummy variables

Functions e(sample) marks estimation sample


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