help xttobit dialog: xttobit
also see: xttobit postestimation
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Title
[XT] xttobit -- Random-effects tobit models
Syntax
xttobit depvar [indepvars] [if] [in] [weight] [, options]
options description
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Model
noconstant suppress constant term
ll(varname|#) left-censoring variable/limit
ul(varname|#) right-censoring variable/limit
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)
tobit perform likelihood-ratio test comparing
against pooled tobit model
noskip perform likelihood-ratio test
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 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] xttobit postestimation for features available after estimation.
Menu
Statistics > Longitudinal/panel data > Censored outcomes > Tobit
regression (RE)
Description
xttobit fits a random-effects tobit models. There is no command for a
conditional fixed-effects model, as there does not exist a sufficient
statistic allowing the fixed effects to be conditioned out of the
likelihood. Honore has developed a semiparametric estimator for
fixed-effect tobit models. Unconditional fixed-effects tobit models may
be fit with tobit command with indicator variables for the panels. The
appropriate indicator variables can be generated using tabulate or xi.
However, unconditional fixed-effects estimates are biased.
Options
+-------+
----+ Model +------------------------------------------------------------
noconstant; see [R] estimation options.
ll(varname|#) and ul(varname|#) indicate the censoring points. You may
specify one or both. ll() indicates the lower limit for
left-censoring. Observations with depvar<ll() are left-censored,
observations with depvar>ul() are right-censored, and remaining
observations are not censored. See [R] tobit.
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(#); see [R] estimation options.
tobit specifies that a likelihood-ratio test comparing the random effects
model with the pooled (tobit) model be included in the output.
noskip; see [R] estimation options.
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. Some of these options are not
available if intmethod(ghermite) is specified. These options are
seldom used.
The following option is available with xttobit 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 xttobit 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.
Example
Setup
. webuse nlswork3
. xtset idcode
Fit random-effects (RE) tobit model
. xttobit ln_wage union age grade not_smsa south##c.year, ul(1.9)
Same as above, but increase the number of integration points from 12 to
25
. xttobit ln_wage union age grade not_smsa south##c.year, ul(1.9)
intpoints(25)
Same as above, but report likelihood-ratio test comparing RE model with
the pooled model
. xttobit ln_wage union age grade not_smsa south##c.year, ul(1.9)
intpoints(25) tobit
Saved results
xttobit 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_cd) number of completely determined observations
e(k) number of 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) xttobit
e(cmdline) command as typed
e(depvar) name of dependent variable
e(ivar) variable denoting groups
e(llopt) contents of ll(), if specified
e(ulopt) contents of ul(), if specified
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] xttobit
Help: [XT] xttobit postestimation;
[XT] quadchk, [XT] xtintreg, [XT] xtreg, [R] tobit
