|
ivreg2 implements a range of single-equation estimation methods for the linear regression model: ordinary least squares
(OLS), instrumental variables (IV, also known as two-stage least squares, 2SLS), the generalized method of moments (GMM),
limited-information maximum likelihood (LIML), and k-class estimators. In the language of IV/GMM, varlist1 are the
exogenous regressors or included instruments, varlist_iv are the exogenous variables excluded from the regression or
excluded instruments, and varlist2 the endogenous regressors that are being instrumented.
ivreg2 will also estimate linear regression models using robust (heteroskedastic-consistent), autocorrelation-consistent
(AC), heteroskedastic and autocorrelation-consistent (HAC) and cluster-robust variance estimates.
ivreg2 provides extensions to Stata's official ivregress and newey. ivreg2 supports the same command syntax as official
ivregress and (almost) all its options. The main extensions available are as follows: two-step feasible GMM estimation
(gmm2s option) and continuously updated GMM estimation (cue option); LIML and k-class estimation; automatic output of
overidentification and underidentification test statistics; C statistic test of exogeneity of subsets of instruments
(orthog() option); endogeneity tests of endogenous regressors (endog() option); test of instrument redundancy (redundant()
option); kernel-based autocorrelation-consistent (AC), and heteroskedastic and autocorrelation consistent (HAC) standard
errors and covariance estimation (bw(#) option), with user-specified choice of kernel (kernel() option); default reporting
of large-sample statistics (z and chi-squared rather than t and F); small option to report small-sample statistics;
first-stage regressions reported with various tests and statistics for identification and instrument relevance; ffirst
option to report only these identification statistics and not the first-stage regression results themselves; nofooter
option to suppress footer of regression output. ivreg2 can also be used for OLS estimation using the same command syntax
as official regress and newey.
xtivreg offers five different estimators for fitting panel-data models in which some of the right-hand-side covariates are
endogenous. These estimators are two-stage least-squares generalizations of simple panel-data estimators for exogenous
variables. xtivreg with the be option uses the two-stage least-squares between estimator. xtivreg with the fe option uses
the two-stage least-squares within estimator. xtivreg with the re option uses a two-stage least-squares random-effects
estimator. There are two implementations: G2SLS from Balestra and Varadharajan-Krishnakumar (1987) and EC2SLS from
Baltagi. The Balestra and Varadharajan-Krishnakumar G2SLS is the default because it is computationally less expensive.
Baltagi's EC2SLS can be obtained by specifying the ec2sls option. xtivreg with the fd option requests the two-stage
least-squares first-differenced estimator.
See Baltagi (2008) for an introduction to panel-data models with endogenous covariates. For the derivation and application
of the first-differenced estimator, see Anderson and Hsiao (1981).
| 
发帖
雷达卡
加好友,备注jltj
京公网安备 11010802022788号
论坛法律顾问：王进律师
知识产权保护声明
免责及隐私声明
