help xtintreg dialog: xtintreg
also see: xtintreg postestimation
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Title
[XT] xtintreg -- Random-effects interval-data regression models
Syntax
xtintreg depvar_lower depvar_upper [indepvars] [if] [in] [weight] [,
options]
options description
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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)
noskip perform likelihood-ratio test
intreg perform likelihood-ratio test against pooled
model
nocnsreport do not display constraints
display_options control spacing and display of omitted
variables and base and empty cells
Integration
intmethod(intmethod) integration method; intmethod may be
mvaghermite, aghermite, or ghermite;
default is intmethod(mvaghermite)
intpoints(#) use # quadrature points; default is
intpoints(12)
Maximization
maximize_options control the maximization process; seldom
used
+ coeflegend display coefficients' legend instead of
coefficient table
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+ coeflegend does not appear in the dialog box.
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.
See [XT] xtintreg postestimation for features available after estimation.
Menu
Statistics > Longitudinal/panel data > Censored outcomes > Interval
regression (RE)
Description
xtintreg fits a random-effects regression model whose dependent variable
may be measured as point data, interval data, left-censored data, or
right-censored data. depvar_lower and depvar_upper represent how the
dependent variable was measured.
The values in depvar_lower and depvar_upper 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 .
--------------------------------------------------------
Options
+-------+
----+ 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 and that use
bootstrap or jackknife methods; see [XT] vce_options.
+-----------+
----+ Reporting +--------------------------------------------------------
level(#), noskip; 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: noomitted, vsquish, noemptycells, baselevels,
allbaselevels; 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,
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.
Examples
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)
Saved results
xtintreg saves 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(N_cd) number of completely determined observations
e(k) number of parameters
e(k_aux) number of auxiliary parameters
e(k_eq) number of equations
e(k_eq_model) number of equations in model Wald test
e(k_dv) number of dependent variables
e(k_autoCns) number of base, empty, and omitted constraints
e(df_m) model degrees of freedom
e(ll) log likelihood
e(ll_0) log likelihood, constant-only 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) significance
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) name of dependent variable
e(ivar) variable denoting groups
e(wtype) weight type
e(wexp) weight expression
e(title) title in estimation output
e(offset1) 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(vcetype) title used to label Std. Err.
e(intmethod) integration method
e(distrib) Gaussian; the distribution of the random effect
e(diparm#) display transformed parameter #
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(singularHmethod) m-marquardt or hybrid; method used when Hessian is
singular
e(crittype) optimization criterion
e(properties) b V
e(predict) program used to implement predict
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
Also see
Manual: [XT] xtintreg
Help: [XT] xtintreg postestimation;
[XT] quadchk, [XT] xtreg, [XT] xttobit, [XT] intreg, [R] tobit
