ivreg2 update

If you’re going to run multiple endogenous variables (not something we’re all that crazy about) you at least oughta look at the appropriate first stage Fs.  And, as explained in an earlier post, we didn’t give the right formula in MHE.  Luckily, a routine for first-stage F-stats in models with multiple endogenous variables is now programmed in ivreg2.  The same update includes other useful routines, like two-way clustering.  More information below:

New versions of and extensions to the Baum-Schaffer-Stillman packages
ivreg2, xtivreg2, ranktest and xtoverid, and a new program, ivreg29, are
now available from ssc.

The main extensions and upgrades are:

1.  2-way clustering.

2-way clustering, introduced by Cameron, Gelbach and Miller (2006) and
Thompson (2009), is now supported.  2-way clustering, e.g.,

	ivreg2 y x1 x2, cluster(id year)

or
	ivreg2 y (x = z1 z2), gmm2s (cluster id year)

allows for arbitrary within-cluster correlation in two cluster
dimensions.  In the examples above, standard errors and statistics are
robust to disturbances that are autocorrelated (correlated within
panels, clustering on id) and common (correlated across panels,
clustering on year).  In the second example, estimates also are
efficient in the presence of arbitrary within-panel and within-year
clustering.  As with 1-way clustering, the numbers of clusters in both
dimensions should be large.

2.  Angrist-Pischke first-stage F statistics

ivreg2 and xtivreg2 now provide Angrist-Pischke first-stage F
statistics.  Angrist and Pischke (2009, pp. 217-18) introduced
first-stage F statistics for tests of under- and weak identification
when there is more than one endogenous regressor.  In contrast to the
Cragg-Donald and Kleibergen-Paap statistics, which test the
identification of the equation as a whole, the AP first-stage F
statistics are tests of whether one of the endogenous regressors is
under- or weakly identified.

3.  SEs that are robust to autocorrelated across-panel disturbances

Following Thompson (2009), cluster-robust and kernel-robust SEs can be
combined and applied to panel data to produce SEs that are robust to
arbitary common autocorrelated disturbances.  This can also be combined
with 2-way clustering to provide SEs and statistics that are robust to
autocorrelated within-panel disturbances (clustering on panel id) and to
autocorrelated across-panel disturbances (clustering on time combined
with kernel-based HAC).

4.  ivreg2 has been Mata-ized

... and is noticably faster, in particular with time series and the CUE
(continuously-updated) GMM estimator.

5.  ivreg29 for users who don't yet have Stata 10 or 11

ivreg2 requires Stata 10 or later.  For those who have only Stata 9, we
have provided a new program, ivreg29.  ivreg29 is basically the previous
version of ivreg2 plus support for AP F-statistics and some minor bug
fixes.  ivreg29 does not support the other features described above.

For full details and examples, see the new help files accompanying the
programs.