Title
[R] ivregress -- Single-equation instrumental-variables regression
Syntax
ivregress estimator depvar [varlist1] (varlist2 = varlist_iv) [if] [in] [weight] [,
options]
estimator Description
-----------------------------------------------------------------------------------------------
2sls two-stage least squares (2SLS)
liml limited-information maximum likelihood (LIML)
gmm generalized method of moments (GMM)
-----------------------------------------------------------------------------------------------
options Description
-----------------------------------------------------------------------------------------------
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 column formats, row spacing, line width, and display of omitted
variables and base and empty cells
perfect do not check for collinearity between endogenous regressors and
excluded instruments
coeflegend display legend instead of statistics
-----------------------------------------------------------------------------------------------
# These options may be specified only when gmm is specified.
* These options may be specified only when igmm is specified.
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.
perfect and coeflegend do not appear in the dialog box.
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 1987) kernel; and
quadraticspectral or andrews requests the quadratic spectral (Andrews 1991) 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. depname 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 depname rather than depvar. depname()
is most typically used when depvar is a temporary variable (see [P] macro) used as a proxy
for depname.
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, cformat(%fmt),
pformat(%fmt), sformat(%fmt), and nolstretch; 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)
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