Kit,
Thank you very much! I have one follow-up question on choosing between xtabond (or xtabond2) and xtivreg2 with cluster-robust SEs plus gmm2s. Under what circumstance would it make sense for me to include lagged dependent variables in the model (and thus considering xtabond)? In my case I am looking at factors affecting insurance premiums across countries (premium is my dependent variable). While I am not interested in how last year's premiums affect current premiums, it is possible that current year's premiums are associated with prior year's premiums, and thus I would like to control for that effect. Would xtivreg2 with cluster-robust SEs be sufficient? Thank you!
Yuluen
________________________________
From: [email protected] on behalf of Kit Baum
Sent: Wed 5/14/2008 9:17 AM
To: [email protected]
Subject: st: Re: GMM or 2SLS with cluster adjustment or others
The Arellano-Bond / Arellano-Bover / Blundell-Bond methodology
(xtabond, xtabond2) is appropriate if you have lagged dependent
variables, and unnecessary otherwise.
If you have a panel with endogenous regressors, use xtivreg2 with
cluster-robust SEs, preferably with the gmm2s option.
See Baum-Schaffer-Stillman, Stata Journal 7:4 2007, for details.
Preprint available from my homepage below as a BC working paper.
Kit Baum, Boston College Economics and DIW Berlin
http://ideas.repec.org/e/pba1.html
An Introduction to Modern Econometrics Using Stata:
http://www.stata-press.com/books/imeus.html
On May 14, 2008, at 02:33 , statalist-digest wrote:
> I have panel data and would like to adjust for potential
> autocorrelation in the dependent variable as well as the
> endogeneity in one of the independent variables. Would 2SLS with
> cluster adjustment (i.e. ivreg with cluster option) be sufficient?
> I was suggested to use the Arellano-Bond (1991)/Arellano-Bover
> (1995) estimation procedure. I assume it is GMM. Would that be
> necessary? How do these two methodologies differ? Thanks.
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