Stata 15 help for xtintreg

[XT] xtintreg -- Random-effects interval-data regression models


xtintreg depvar_lower depvar_upper [indepvars] [if] [in] [weight] [, options]

The values in depvar_lower and depvar_upper should have the following form:

Type of data depvar_lower depvar_upper -------------------------------------------------------- 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 . . --------------------------------------------------------

options Description ------------------------------------------------------------------------- Model noconstant suppress constant term offset(varname) include varname in model with coefficient constrained to 1 constraints(constraints) apply specified linear constraints collinear keep collinear variables

SE vce(vcetype) vcetype may be oim, 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 intreg perform likelihood-ratio test against pooled model 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 intmethod(intmethod) integration method; intmethod may be mvaghermite (the default) or ghermite intpoints(#) use # quadrature points; default is intpoints(12) Maximization maximize_options control the maximization process; seldom used

coeflegend display legend instead of statistics ------------------------------------------------------------------------- A panel variable must be specified; use xtset. indepvars may contain factor variables; see fvvarlist. depvar_lower, depvar_upper, and indepvars may contain time-series operators; see tsvarlist. by and statsby are allowed; see prefix. iweights are allowed; see weight. Weights must be constant within panel. coeflegend does not appear in the dialog box. See [XT] xtintreg postestimation for features available after estimation.


Statistics > Longitudinal/panel data > Censored outcomes > Interval regression (RE)


xtintreg fits a random-effects regression model in which the dependent variable may be measured as point data, interval data, left-censored data, or right-censored data. The dependent variable must be specified using two depvars that indicate how the dependent variable was measured. The user can request that a likelihood-ratio test comparing the panel interval regression model with the pooled model be conducted at estimation time.


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

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

+----+ ----+ SE +---------------------------------------------------------------

vce(vcetype) specifies the type of standard error reported, which includes types that are derived from asymptotic theory (oim) and that use bootstrap or jackknife methods (bootstrap, jackknife); see [XT] vce_options.

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

level(#), lrmodel; see [R] estimation options.

intreg specifies that a likelihood-ratio test comparing the random-effects model with the pooled (intreg) model be included in the output.

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 +------------------------------------------------------

intmethod(intmethod), intpoints(#); 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 xtintreg but is not shown in the dialog box:

coeflegend; see [R] estimation options.

Technical note

The random-effects model is calculated using quadrature, which is an approximation whose accuracy depends partially on the number of integration points used. We can use the quadchk command to see if changing the number of integration points affects the results. If the results change, the quadrature approximation is not accurate given the number of integration points. Try increasing the number of integration points using the intpoints() option and again run quadchk. Do not attempt to interpret the results of estimates when the coefficients reported by quadchk differ substantially. See [XT] quadchk for details and [XT] xtprobit for an example.

Because the xtintreg likelihood function is calculated by Gauss-Hermite quadrature, on large problems, the computations can be slow. Computation time is roughly proportional to the number of points used for the quadrature.


Setup . webuse nlswork5 . xtset idcode

Fit random-effects (RE) interval-data regression model . xtintreg ln_wage1 ln_wage2 union age grade south##c.year occ_code

Same as above, but include likelihood-ratio test comparing RE model with the pooled model . xtintreg ln_wage1 ln_wage2 union age grade south##c.year occ_code, intreg

Fit RE interval-data regression model using nonadaptive Gauss-Hermite quadrature . xtintreg ln_wage1 ln_wage2 union age grade south##c.year occ_code, intmethod(ghermite)

Replay results and report 99.5% confidence intervals . xtintreg, level(99.5)

Stored results

xtintreg stores the following in e():

Scalars e(N) number of observations e(N_g) number of groups 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 observations e(k) number of parameters e(k_aux) number of auxiliary parameters 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(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(chi2) chi-squared e(chi2_c) chi-squared for comparison test e(rho) rho e(sigma_u) panel-level standard deviation e(sigma_e) standard deviation of epsilon_it e(n_quad) number of quadrature points e(g_min) smallest group size e(g_avg) average group size e(g_max) largest group size e(p) p-value for model test 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) xtintreg e(cmdline) command as typed e(depvar) names of dependent variables e(ivar) variable denoting groups e(wtype) weight type e(wexp) weight expression e(title) title in estimation output e(offset) offset 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(intmethod) integration method e(distrib) Gaussian; the distribution of the random effect 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 e(gradient) gradient vector e(V) variance-covariance matrix of the estimators

Functions e(sample) marks estimation sample

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