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 overall model test as a likelihood-ratio test
nocnsreport do not display constraints
display_options control 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
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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, fp, 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] xttobit postestimation for features available after estimation.
xttobit fits a random-effects tobit models. There is no command for a parametric conditional fixed-effects model, as there does not exist a sufficient statistic allowing the fixed effects to be conditioned out of the likelihood. Honor?(1992) has developed a semiparametric estimator for fixed-effect tobit models. Unconditional fixed-effects tobit models may be fit with the tobit command with indicator variables for the panels; the indicators can be created with the factor-variable syntax described in [U] 11.4.3 Factor variables. However, unconditional fixed-effects estimates are biased.