# st: re: How to correct standard errors of a 2sls performed by

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## st: re: How to correct standard errors of a 2sls performed by

 <> John wrote How would one deal with systems of equations (i.e., reg3) and concurrently have heteroskedastic-robust standard errors?  Would bootstrapping with reg3 just be the simplest solution? In the absence of cross-equation constraints, just estimate each equation with single-equation methods, i.e. -ivregress- or -ivreg2- using robust or cluster-robust VCE. Nothing is lost in doing so beyond a possible gain in efficiency. Kit Kit Baum   |   Boston College Economics & DIW Berlin   |   http://ideas.repec.org/e/pba1.html                              An Introduction to Stata Programming  |   http://www.stata-press.com/books/isp.html   An Introduction to Modern Econometrics Using Stata  |   http://www.stata-press.com/books/imeus.html* *   For searches and help try: *   http://www.stata.com/help.cgi?search*   http://www.stata.com/support/statalist/faq*   http://www.ats.ucla.edu/stat/stata/
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## Re: st: re: How to correct standard errors of a 2sls performed by

 Hi: What if the system is non-recursive (with feedback loops) and with multiple equations, e.g., y = x1 + x2 + z x1 = m1 + m2 + x2 + z x2 = n1 + n2 + x1 + z m1 = q1 + q2 + z m2 = p1 + p2 + z Here one would need reg3, but how would one ensure consistency of standard errors (in the presence of heteroskedasticity), apart from bootstrapping (and Roodman's -cmp- would not here as it is only for recursive systems)? Thanks, J. ____________________________________________________ Prof. John Antonakis, Associate Dean Faculty of Business and Economics Department of Organizational Behavior University of Lausanne Internef #618 CH-1015 Lausanne-Dorigny Switzerland Tel ++41 (0)21 692-3438 Fax ++41 (0)21 692-3305 Faculty page: http://www.hec.unil.ch/people/jantonakisPersonal page: http://www.hec.unil.ch/jantonakis____________________________________________________ On 06.02.2010 02:02, Kit Baum wrote: > <> > John wrote > How would one deal with systems of equations (i.e., reg3) and > concurrently have heteroskedastic-robust standard errors?  Would > bootstrapping with reg3 just be the simplest solution? > > > In the absence of cross-equation constraints, just estimate each equation with single-equation methods, i.e. -ivregress- or -ivreg2- using robust or cluster-robust VCE. Nothing is lost in doing so beyond a possible gain in efficiency. > > Kit > > Kit Baum   |   Boston College Economics & DIW Berlin   |   http://ideas.repec.org/e/pba1.html>                               An Introduction to Stata Programming  |   http://www.stata-press.com/books/isp.html>    An Introduction to Modern Econometrics Using Stata  |   http://www.stata-press.com/books/imeus.html> > > * > *   For searches and help try: > *   http://www.stata.com/help.cgi?search> *   http://www.stata.com/support/statalist/faq> *   http://www.ats.ucla.edu/stat/stata/>   * *   For searches and help try: *   http://www.stata.com/help.cgi?search*   http://www.stata.com/support/statalist/faq*   http://www.ats.ucla.edu/stat/stata/
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## st: New versions of/extensions to ivreg2, xtivreg2, ranktest, xtoverid, ivreg29

 Dear Statalisters: 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. --Kit, Mark and Steve References Angrist, J.D. and Pischke, J.-S. 2009. Mostly Harmless Econometrics: An Empiricist's Companion.  Princeton: Princeton University Press. Cameron, A.C., Gelbach, J.B. and Miller, D.L.  2006.  Robust Inference with Multi-Way Clustering.  NBER Technical Working paper 327. http://www.nber.org/papers/t0327. Thompson, S.B.  2009.  Simple Formulas for Standard Errors that Cluster by Both Firm and Time.  http://ssrn.com/abstract=914002. Prof. Mark Schaffer FRSE Department of Economics School of Management & Languages Heriot-Watt University, Edinburgh EH14 4AS tel +44-131-451-3494 / fax +44-131-451-3296 http://ideas.repec.org/e/psc51.html  -- Heriot-Watt University is a Scottish charity registered under charity number SC000278. * *   For searches and help try: *   http://www.stata.com/help.cgi?search*   http://www.stata.com/support/statalist/faq*   http://www.ats.ucla.edu/stat/stata/