Jaime,
> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]] On Behalf Of
> Jaime Gómez
> Sent: 12 October 2009 23:34
> To: [email protected]
> Subject: st: RE: RE: Instrumental variables and panel data
>
> Dear Mark
>
> Thank you very much for your message. The problem is that
> (with xtoverid) I do not know any way to ascertain whether
> the possibly endogenous variable is exogenous or whether I
> have a weak instruments problem (or whether the random
> effects estimates are preferred over the fixed). With
> xtoverid, is there any way to know the estimates I have to
> rely on?.
The discussion on Statalist about -xtoverid- that I mentioned included a discussion of how to hack the code to make it do things like endogeneity tests. I think that Austin Nichols even provided a link to a downloadable -xtoverid2- that would do this. -xtoverid- calls -ivreg2- internally, and you can see from the discussion how to hack the internal call to -ivreg2- to do what you want it to do.
Happy hacking!
Cheers,
Mark
> In fact, using the
> ivreg2 command with the endog( ) option shows that the
> variable is not endogenous, but this is not a panel data
> estimation and I do not know whether, from the ivreg2
> estimation, I can simply conclude that there is not an
> endogeneity problem. In any case, I still would have to solve
> the problem of getting the coefficients of the time-invariant
> dummies if the Hausman test indicates that the fixed effects
> is the preferred estimation (could xthaylor provide a
> consistent solution?).
>
> On the other hand, I have been suggested to estimate GMM
> System through xtabond2, but reading David Roodman's paper,
> it seems to me that the context in which this is applied is
> different (1. I have dummy variables that could bias the
> results; 2. I have 59 firms followed an average of 25
> quarterly periods; 3. I have a good external instrument; 4. I
> do not have lags of dependent variables as regressors).
> Please, any advise on this?
>
> Thanks !
>
> Jaime.
>
>
>
>
> -----Mensaje original-----
> De: [email protected]
> [mailto:[email protected]] En nombre de
> Schaffer, Mark E Enviado el: jueves, 08 de octubre de 2009 16:22
> Para: [email protected]
> Asunto: st: RE: Instrumental variables and panel data
>
> Jaime,
>
> > -----Original Message-----
> > From: [email protected]
> > [mailto:[email protected]] On Behalf Of
> Jaime Gómez
> > Sent: 06 October 2009 23:13
> > To: [email protected]
> > Subject: st: Instrumental variables and panel data
> >
> > Dear Statalisters
> >
> > We have a model in which firm performance depends on (1)
> the order of
> > entry and (2) a possibly endogenous variable and (3) other
> variables,
> > including time dummies. First, we were suggested to use
> instrumental
> > variable techniques and to provide HAC standard errors,
> something we
> > have already done with the ivreg2 command in Stata and using an
> > external instrument. We tested for the exogeneity of the possibly
> > endogenous variable through the endog( ) option and the test shows
> > that the variable could be considered exogenous.
> >
> > In a second step, we have been suggested to use the panel
> structure of
> > our data and, simultaneously, to consider the endogeneity problem.
> > Ideally, we would like (1) to estimate a panel data model with
> > instrumental variables and HAC errors,
> > (2) to test for the exogeneity of our possible endogenous
> variable and
> > (3) to check whether the fixed or random effects model is
> appropriate.
> > So, it seems that the xtivreg or
> > xtivreg2 commands could be the solution. Nevertheless, we
> have several
> > problems:
> >
> > 1) the order of entry is represented through time invariant dummies
> > (pioneer, second mover, third mover, ...) that drop when we
> estimate a
> > fixed effects model, but we are (very) interested in the
> values of the
> > coefficients. So it seems that the only way of getting these
> > coefficients is to estimate a random effects model and
> check whether
> > this is appropriate with a Hausman test (If I reject the random
> > effects model, ¿could I get the order of entry coefficients through
> > another panel data technique?)
> >
> > 2) Before doing so we have to find the way of getting HAC standard
> > errors. I think I would know how to do this with
> > xtivreg2 (I am assuming that the options are similar to the ones in
> > ivreg2), nevertheless it seems that there is no way of estimating a
> > random effects model with xtivreg2. The problem with using xtivreg
> > seems that the estimation and postestimation options are much more
> > restricted than with
> > xtivreg2 (for example, how do I get HAC errors? How do I
> test for the
> > endogeneity of the regressor? Should I use xtoverid for testing for
> > the appropriateness of the random effects model?).
> >
> > In summary, is there any way for treating all these issues
> (possibly
> > omitted variables that advise the use of panel data
> techniques, time
> > invariant variables of interest, HAC standard errors and
> instrumental
> > variables) at the same time?
> > Alternatively, could you suggest another strategy to tackle all the
> > problems with Stata (perhaps sequentially?)?
>
> A couple of thoughts...
>
> 1. You can use -xtoverid- with the undocumented -noisily-
> option to estimate a random effects model with various types
> of robust SEs. There have been several threads on Statalist
> about it, so it should be pretty easy to find. (I really
> have to get around to making -xtivreg2- do random
> effects....)
>
> 2. Cluster-robust SEs are robust to arbitrary within-cluster
> correlation as well as heteroskedasticity, and you can think
> of them as a variety of HAC SEs. The main difference between
> them and the usual kernel-based HAC SEs (as supported by
> -xtivreg2- et al.) is that the asymptotics for cluster-robust
> SEs have the number of clusters going off to infinity; the
> asymptotics for the usual kernel HAC SEs (Bartlett kernel aka
> Newey-West and all those guys) is that they require time to
> go off to infinity. Most panels these days are
> small-T-large-N, so chances are you would be better off with
> cluster-robust. Of course, it's up to you.
>
> Cheers,
> Mark
>
> > Thanks a lot
> > Sincerely
> > Jaime Gómez
> > Universidad de Zaragoza
> >
> >
> > *
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> >
>
>
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>
>
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registered under charity number SC000278.
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