help truncreg dialogs: truncreg svy: truncreg
also see: truncreg postestimation
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Title
[R] truncreg -- Truncated regression
Syntax
truncreg depvar [indepvars] [if] [in] [weight] [, options]
options description
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Model
noconstant suppress constant term
ll(varname|#) lower limit for left truncation
ul(varname|#) upper limit for right truncation
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)
noskip perform likelihood-ratio test
nocnsreport do not display constraints
display_options control spacing and display of omitted
variables and base and empty cells
Maximization
maximize_options control maximization process; seldom used
+ coeflegend display coefficients' legend instead of
coefficient table
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+ coeflegend does not appear in the dialog box.
indepvars may contain factor variables; see fvvarlist.
depvar and indepvars may contain time-series operators; see tsvarlist.
bootstrap, by, jackknife, mi estimate, rolling, statsby, and svy are
allowed; see prefix.
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(), noskip, and weights are not allowed with the svy prefix.
aweights, fweights, iweights, and pweights are allowed; see weight.
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 varlist 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 upper and lower 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, that are
robust to some kinds of misspecification, that allow for intragroup
correlation, and that use bootstrap or jackknife methods; see [R]
vce_option.
+-----------+
----+ Reporting +--------------------------------------------------------
level(#); see [R] estimation options.
noskip specifies that a full maximum-likelihood model with only a
constant for the regression equation be fit. This model is not
displayed but is used as the base model to compute a likelihood-ratio
test for the model test statistic displayed in the estimation header.
By default, the overall model test statistic is an asymptotically
equivalent Wald test of all the parameters in the regression equation
being zero (except the constant). For many models, this option can
substantially increase estimation time.
nocnsreport; see [R] estimation options.
display_options: noomitted, vsquish, noemptycells, baselevels,
allbaselevels; 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,
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
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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)
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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)
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Saved results
truncreg saves 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 model Wald test
e(k_aux) number of auxiliary parameters
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(N_clust) number of clusters
e(sigma) estimate of sigma
e(p) significance
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(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 (up to 20 iterations)
e(gradient) gradient vector
e(V) variance-covariance matrix of the estimators
e(V_modelbased) model-based variance
e(ml_h) derivative tolerance, (abs(b)+1e-3)*1e-3
e(ml_scale) derivative scale factor
e(means) means of independent variables
e(dummy) indicator for dummy variables
Functions
e(sample) marks estimation sample
Also see
Manual: [R] truncreg
Help: [R] truncreg postestimation;
[R] regress, [R] tobit
