help ivregress dialog: ivregress
also see: ivregress postestimation
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Title
[R] ivregress -- Single-equation instrumental-variables regression
Syntax
ivregress estimator depvar [varlist1] (varlist2 = varlist_iv) [if] [
in] [weight] [, options]
estimator description
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2sls two-stage least squares (2SLS)
liml limited-information maximum likelihood (LIML)
gmm generalized method of moments (GMM)
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options description
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Model
noconstant suppress constant term
hascons has user-supplied constant
+ GMM
wmatrix(wmtype) wmtype may be robust, cluster clustvar, hac
kernel, or unadjusted
center center moments in weight matrix computation
igmm use iterative instead of two-step GMM estimator
* eps(#) specify # for parameter convergence criterion;
default is eps(1e-6)
* weps(#) specify # for weight matrix convergence
criterion; default is weps(1e-6)
* optimization options control the optimization process; seldom used
SE/Robust
vce(vcetype) vcetype may be unadjusted, robust, cluster
clustvar, bootstrap, jackknife, or hac kernel
Reporting
level(#) set confidence level; default is level(95)
first report first-stage estimates
small make degrees-of-freedom adjustments and report
small-sample statistics
noheader display only the coefficient table
depname(depname) substitute dependent variable name
eform(string) report exponentiated coefficients and use string
to label them
display_options control spacing and display of omitted variables
and base and empty cells
# perfect do not check for collinearity between endogenous
regressors and excluded instruments
# coeflegend display coefficients' legend instead of
coefficient table
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+ These options may be specified only when gmm is specified.
* These options may be specified only when igmm is specified.
# perfect and coeflegend do not appear in the dialog box.
varlist1 and varlist_iv may contain factor variables; see fvvarlist.
depvar, varlist1, varlist2, and varlist_iv may contain time-series
operators; see tsvarlist.
bootstrap, by, jackknife, rolling, statsby, and svy are allowed; see
prefix.
Weights are not allowed with the bootstrap prefix.
aweights are not allowed with the jackknife prefix.
hascons, vce(), noheader, depname(), and weights are not allowed with the
svy prefix.
aweights, fweights, iweights, and pweights are allowed; see weight.
See [R] ivregress postestimation for features available after estimation.
Menu
Statistics > Endogenous covariates > Single-equation
instrumental-variables regression
Description
ivregress fits a linear regression of depvar on varlist1 and varlist2,
using varlist_iv (along with varlist1) as instruments for varlist2.
ivregress supports estimation via two-stage least squares (2SLS),
limited-information maximum likelihood (LIML), and generalized method of
moments (GMM).
In the language of instrumental variables, varlist1 and varlist_iv are
the exogenous variables, and varlist2 are the endogenous variables.
Options
+-------+
----+ Model +------------------------------------------------------------
noconstant; see [R] estimation options.
hascons indicates that a user-defined constant or its equivalent is
specified among the independent variables.
+-----+
----+ GMM +--------------------------------------------------------------
wmatrix(wmtype) specifies the type of weighting matrix to be used in
conjunction with the GMM estimator.
Specifying wmatrix(robust) requests a weighting matrix that is
optimal when the error term is heteroskedastic. wmatrix(robust) is
the default.
Specifying wmatrix(cluster clustvar) requests a weighting matrix that
accounts for arbitrary correlation among observations within clusters
identified by clustvar.
Specifying wmatrix(hac kernel #) requests a heteroskedasticity- and
autocorrelation-consistent (HAC) weighting matrix using the specified
kernel (see below) with # lags. The bandwidth of a kernel is equal
to the number of lags plus one.
Specifying wmatrix(hac kernel opt) requests an HAC weighting matrix
using the specified kernel, and the lag order is selected using Newey
and West's (1994) optimal lag-selection algorithm.
Specifying wmatrix(hac kernel) requests an HAC weighting matrix using
the specified kernel and N-2 lags, where N is the sample size.
There are three kernels available for HAC weighting matrices, and you
may request each one by using the name used by statisticians or the
name perhaps more familiar to economists:
bartlett or nwest requests the Bartlett (Newey-West) kernel;
parzen or gallant requests the Parzen (Gallant) kernel; and
quadraticspectral or andrews requests the quadratic spectral
(Andrews) kernel.
Specifying wmatrix(unadjusted) requests a weighting matrix that is
suitable when the errors are homoskedastic. The GMM estimator with
this weighting matrix is equivalent to the 2SLS estimator.
center requests that the sample moments be centered (demeaned) when
computing GMM weight matrices. By default, centering is not done.
igmm requests that the iterative GMM estimator be used instead of the
default two-step GMM estimator. Convergence is declared when the
relative change in the parameter vector from one iteration to the
next is less than eps() or the relative change in the weight matrix
is less than weps().
eps(#) specifies the convergence criterion for successive parameter
estimates when the iterative GMM estimator is used. The default is
eps(1e-6). Convergence is declared when the relative difference
between successive parameter estimates is less than eps() and the
relative difference between successive estimates of the weighting
matrix is less than weps().
weps(#) specifies the convergence criterion for successive estimates of
the weighting matrix when the iterative GMM estimator is used. The
default is weps(1e-6). Convergence is declared when the relative
difference between successive parameter estimates is less than eps()
and the relative difference between successive estimates of the
weighting matrix is less than weps().
optimization_options: iterate(), nolog. iterate() specifies the maximum
number of iterations to perform in conjunction with the iterative GMM
estimator. The default is 16,000 or the number set using set
maxiter. log/nolog specifies whether to show the iteration log.
These options are seldom used.
+-----------+
----+ SE/Robust +--------------------------------------------------------
vce(vcetype) specifies the type of standard error reported, which
includes types 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(unadjusted), the default for 2sls and liml, specifies that an
unadjusted (nonrobust) VCE matrix be used. The default for gmm is
based on the wmtype specified in the wmatrix() option; see
wmatrix(wmtype) above. If wmatrix() is specified with gmm but vce()
is not, then vcetype is set equal to wmtype. To override this
behavior and obtain an unadjusted (nonrobust) VCE matrix, specify
vce(unadjusted).
ivregress also allows the following:
vce(hac kernel [# | opt]) specifies that an HAC covariance matrix be
used. The syntax used with vce(hac kernel ...) is identical to
that used with wmatrix(hac kernel ... ); see wmatrix(wmtype)
above.
+-----------+
----+ Reporting +--------------------------------------------------------
level(#); see [R] estimation options.
first requests that the first-stage regression results be displayed.
small requests that the degrees-of-freedom adjustment N/(N-k) be made to
the variance-covariance matrix of parameters and that small-sample F
and t statistics be reported, where N is the sample size and k is the
number of parameters estimated. By default, no degrees-of-freedom
adjustment is made, and Wald and z statistics are reported. Even with
this option, no degrees-of-freedom adjustment is made to the
weighting matrix when the GMM estimator is used.
noheader suppresses the display of the summary statistics at the top of
the output, displaying only the coefficient table.
depname(depname) is used only in programs and ado-files that use
ivregress to fit models other than instrumental-variables regression.
depname() may be specified only at estimation time. varname is
recorded as the identity of the dependent variable, even though the
estimates are calculated using depvar. This method affects the
labeling of the output -- not the results calculated -- but could
affect later calculations made by predict, where the residual would
be calculated as deviations from varname rather than depvar.
depname() is most typically used when depvar is a temporary variable
(see [P] macro) used as a proxy for varname.
eform(string) is used only in programs and ado-files that use ivregress
to fit models other than instrumental-variables regression. eform()
specifies that the coefficient table be displayed in "exponentiated
form", as defined in [R] maximize, and that string be used to label
the exponentiated coefficients in the table.
display_options: noomitted, vsquish, noemptycells, baselevels,
allbaselevels; see [R] estimation options.
The following options are available with ivregress but are not shown in
the dialog box:
perfect requests that ivregress not check for collinearity between the
endogenous regressors and excluded instruments, allowing one to
specify "perfect" instruments. This option cannot be used with the
LIML estimator. This option may be required when using ivregress to
implement other estimators.
coeflegend; see [R] estimation options.
Examples
Setup
. webuse hsng2
Fit a regression via 2SLS, requesting small-sample statistics
. ivregress 2sls rent pcturban (hsngval = faminc i.region), small
Fit a regression using the LIML estimator
. ivregress liml rent pcturban (hsngval = faminc i.region)
Fit a regression via GMM using the default heteroskedasticity-robust
weight matrix
. ivregress gmm rent pcturban (hsngval = faminc i.region)
Fit a regression via GMM using a heteroskedasticity-robust weight matrix,
requesting nonrobust standard errors
. ivregress gmm rent pcturban (hsngval = faminc i.region),
vce(unadjusted)
Saved results
ivregress saves the following in e():
Scalars
e(N) number of observations
e(mss) model 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(F) F statistic
e(rmse) root mean squared error
e(N_clust) number of clusters
e(chi2) chi-squared statistic
e(kappa) kappa used in LIML estimator
e(J) value of GMM objective function
e(wlagopt) lags used in HAC weight matrix (if Newey-West
algorithm used)
e(vcelagopt) lags used in HAC VCE matrix (if Newey-West
algorithm used)
e(rank) rank of e(V)
e(iterations) number of GMM iterations (0 if not applicable)
Macros
e(cmd) ivregress
e(cmdline) command as typed
e(depvar) name of dependent variable
e(instd) instrumented variable
e(insts) instruments
e(constant) noconstant or hasconstant if specified
e(wtype) weight type
e(wexp) weight expression
e(title) title in estimation output
e(clustvar) name of cluster variable
e(hac_kernel) HAC kernel
e(hac_lag) HAC lag
e(vce) vcetype specified in vce()
e(vcetype) title used to label Std. Err.
e(robust_prolog) program to prepare estimates for linearized VCE
computations
e(robust_epilog) program to finalize estimates after linearized VCE
computations
e(estimator) 2sls, liml, or gmm
e(exogr) exogenous regressors
e(wmatrix) wmtype specified in wmatrix()
e(moments) centered if center specified
e(small) small if small-sample statistics
e(depname) depname if depname(depname) specified; otherwise
same as e(depvar)
e(properties) b V
e(estat_cmd) program used to implement estat
e(predict) program used to implement predict
e(footnote) program used to implement footnote display
e(marginsok) predictions allowed by margins
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(W) weight matrix used to compute GMM estimates
e(S) moment covariance matrix used to compute GMM
variance-covariance matrix
e(V) variance-covariance matrix of the estimators
e(V_modelbased) model-based variance
Functions
e(sample) marks estimation sample
Also see
Manual: [R] ivregress
Help: [R] ivregress postestimation;
[R] gmm, [R] ivprobit, [R] ivtobit, [R] reg3, [R] regress, [XT]
xtivreg
