help areg dialog: areg
also see: areg postestimation
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Title
[R] areg -- Linear regression with a large dummy-variable set
Syntax
areg depvar [indepvars] [if] [in] [weight], absorb(varname) [options]
options description
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Model
* absorb(varname) categorical variable to be absorbed
SE/Robust
vce(vcetype) vcetype may be ols, robust, cluster clustvar,
bootstrap, or jackknife
Reporting
level(#) set confidence level; default is level(95)
display_options control spacing and display of omitted variables
and base and empty cells
+ coeflegend display coefficients' legend instead of coefficient
table
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* absorb(varname) is required.
+ 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, and statsby 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.
aweights, fweights, and pweights are allowed; see weight.
See [R] areg postestimation for features available after estimation.
Menu
Statistics > Linear models and related > Other > Linear regression
absorbing one cat. variable
Description
areg fits a linear regression absorbing one categorical factor. areg is
designed for datasets with many groups, but not a number of groups that
increases with the sample size. See the xtreg, fe command in [XT] xtreg
for an estimator that handles the case in which the number of groups
increases with the sample size.
Options
+-------+
----+ Model +------------------------------------------------------------
absorb(varname) specifies the categorical variable, which is to be
included in the regression as if it were specified by dummy
variables. absorb() is required.
+-----------+
----+ 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.
vce(ols), the default, uses the standard variance estimator for
ordinary least-squares regression.
Exercise caution when using the vce(cluster clustvar) option with
areg. The effective number of degrees of freedom for the robust
variance estimator is n_g - 1, where n_g is the number of clusters.
Thus the number of levels of the absorb() variable should not exceed
the number of clusters.
+-----------+
----+ Reporting +--------------------------------------------------------
level(#); see [R] estimation options.
display_options: noomitted, vsquish, noemptycells, baselevels,
allbaselevels; see [R] estimation options.
The following option is available with areg but is not shown in the
dialog box:
coeflegend; see [R] estimation options.
Examples
Setup
. sysuse auto
Regression with fixed effects for rep78
. areg price weight length, absorb(rep78)
Same as above, but also compute the bootstrap standard errors
. areg price weight length, absorb(rep78) vce(bootstrap, reps(200))
Saved results
areg saves the following in e():
Scalars
e(N) number of observations
e(tss) total sum of squares
e(df_m) model degrees of freedom
e(rss) residual sum of squares
e(df_r) residual degrees of freedom
e(r2) R-squared
e(r2_a) adjusted R-squared
e(df_a) degrees of freedom for absorbed effect
e(rmse) root mean squared error
e(ll) log likelihood
e(ll_0) log likelihood, constant-only model
e(N_clust) number of clusters
e(F) F statistic
e(F_absorb) F statistic for absorbed effect (when
vce(robust) is not specified)
e(rank) rank of e(V)
Macros
e(cmd) areg
e(cmdline) command as typed
e(depvar) name of dependent variable
e(absvar) name of absorb variable
e(wtype) weight type
e(wexp) weight expression
e(title) title in estimation output
e(clustvar) name of cluster variable
e(vce) vcetype specified in vce()
e(vcetype) title used to label Std. Err.
e(datasignature) the checksum
e(datasignaturevars) variables used in calculation of checksum
e(properties) b V
e(predict) program used to implement predict
e(marginsnotok) predictions disallowed by margins
e(asbalanced) factor variables fvset as asbalanced
e(asobserved) factor variables fvset as asobserved
Matrices
e(b) coefficient vector
e(V) variance-covariance matrix of the estimators
e(V_modelbased) model-based variance
Functions
e(sample) marks estimation sample
Also see
Manual: [R] areg
Help: [R] areg postestimation;
[R] regress, [XT] xtreg
