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st: AW: RE: xtabond2: gmm in levels only meaningful?
Thank you very much Jean. I have not seen the problem from this perspective.
In my case problem 1 and 2 are both an issue and I need the equation in
differences to get rid of the individual effect.
This clarifies, that using GMM for equation in levels only does not make
sense in my case.
[mailto:email@example.com] Im Auftrag von Salvati, Jean
Gesendet: Donnerstag, 5. Januar 2006 17:04
Betreff: st: RE: xtabond2: gmm in levels only meaningful?
There are two potential problems in a model with an individual effect:
1) Correlation between some regressors and the individual effect;
2) Correlation between some regressors and the idiosyncratic
First-differencing solves problem 1). IV estimation (2SLS or, more
generally, GMM) solves problem 2).
When the first lag of the dependent variable is used as a regressor, then
first-differencing solves problem 1) but generates problem 2). In this case,
using instrumental variables is required for consistency. This is the
foundation of the Anderson-Hsiao IV/2SLS estimator and of the Arellano-Bond
When 1) is not an issue for any of the regressors (that is, when all the
regressors are orthogonal to the individual effect), then you can estimate
the levels equation directly (without first-differencing). This is the
so-called "random effect" estimator. There are GLS and 2SLS/IV versions of
this estimator. If you only want to use the levels equation, then you should
consider the random-effect estimator and ask yourself whether your
regressors are all orthogonal to the individual effect. If you really want
to use GMM, then just remember that 2SLS can be seen as a special case of
When some (but not all) of the regressors are orthogonal to the individual
effect, you can use the levels equation to establish orthogonality
conditions that can be combined with the orthogonality conditions for the
first-difference equation. This leads to the Arellano-Bover/Blundell-Bond
GMM estimator implemented in xtabond2.
To summarize, if 1) is not an issue in your model, then you don't need
first-differencing, and you don't need the kind of GMM implemented in
xtabond and xtabond2. In this case, you should look at the "random-effect"
estimators implemented in xtreg, xtivreg, and xtivreg2/ivreg2.
If 1) is an issue, then you can only use the regressors that are orthogonal
to the individual effect to derive orthogonality conditions from the levels
> -----Original Message-----
> From: firstname.lastname@example.org
> [mailto:email@example.com] On Behalf Of Niko Wrede
> Sent: Thursday, January 05, 2006 10:32 AM
> To: firstname.lastname@example.org
> Subject: st: xtabond2: gmm in levels only meaningful?
> Dear all,
> the System-GMM estimator exploits moment conditions for
> equation in differences AND for equation in levels.
> However, using xtabond2
> ..., gmm(depvar, lag(2 .) equ(BOTH)) ...
> I do not get plausibel results (in almost all cases the
> Hansen J Test indicates overidentifying restrictions).
> But when using xtabond2
> ..., gmm(d.depvar, lag(2 .) equ(LEVEL)) ...
> [WITHOUT an additional gmm(depvar, lag(2 .) equ(diff)
> command] the results are economically plausibel and the
> Hansen J Test is ok.
> My understanding of GMM is, that one has to use the equation
> in differences to get rid of the individual effects. In
> addition, one can exploit the equation in levels and use
> differences of the depvar as instruments for those.
> But I have not seen GMM estimators, that use ONLY the
> equation in levels.
> However, maybe anybody knows of such cases and is able to
> give some references.
> I would greatly appreciate any help since I already spent a
> lot of time searching for valuable information on this topic.
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