Stata 15 help for ivregress

[R] ivregress -- Single-equation instrumental-variables regression

Syntax

ivregress estimator depvar [varlist1] (varlist2 = varlist_iv) [if] [ in] [weight] [, options]

varlist1 is the list of exogenous variables.

varlist2 is the list of endogenous variables.

varlist_iv is the list of exogenous variables used with varlist1 as instruments for varlist2.

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 regression 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 columns and column formats, row spacing, line width, display of omitted variables and base and empty cells, and factor-variable labeling

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, varlist2, and varlist_iv may contain factor variables; see fvvarlist. depvar, varlist1, varlist2, and varlist_iv may contain time-series operators; see tsvarlist. bootstrap, by, fmm, jackknife, rolling, statsby, and svy are allowed; see prefix. For more details, see [FMM] fmm ivregress. 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 > Linear regression with endogenous covariates

Description

ivregress fits linear models where one or more of the regressors are endogenously determined. ivregress supports estimation via two-stage least squares (2SLS), limited-information maximum likelihood (LIML), and generalized method of moments (GMM).

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 # + 1.

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. # is an optional tuning parameter that affects the lag order selected; see the discussion in [R] ivregress.

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(), [no]log. 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 (robust), that allow for intragroup correlation (cluster clustvar), and that use bootstrap or jackknife methods (bootstrap, jackknife); 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: noci, nopvalues, noomitted, vsquish, noemptycells, baselevels, allbaselevels, nofvlabel, fvwrap(#), fvwrapon(style), 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)

Fit a regression via 2SLS, with an endogenous factorial interaction . ivregress 2sls rent pcturban (c.popgrow##c.popgrow = c.faminc##c.faminc i.region)

Video example

Instrumental variables regression using Stata

Stored results

ivregress stores 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 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(hac_lag) HAC lag 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(vce) vcetype specified in vce() e(vcetype) title used to label Std. Err. 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(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

References

Andrews, D. W. K. 1991. Heteroskedasticity and autocorrelation consistent covariance matrix estimation. Econometrics 59: 817-858.

Gallant, A. R. 1987. Nonlinear Statistical Models. New York: Wiley.

Newey, W. K., and K. D. West. 1994. Automatic lag selection in covariance matrix estimation. Review of Economic Studies 61: 631-653.


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