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st: RE: RE: RE: Instrumental variables and panel data


From   "Schaffer, Mark E" <M.E.Schaffer@hw.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: RE: RE: RE: Instrumental variables and panel data
Date   Mon, 12 Oct 2009 23:56:27 +0100

Jaime,

> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu 
> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of 
> Jaime Gómez 
> Sent: 12 October 2009 23:34
> To: statalist@hsphsun2.harvard.edu
> 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: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu] En nombre de 
> Schaffer, Mark E Enviado el: jueves, 08 de octubre de 2009 16:22
> Para: statalist@hsphsun2.harvard.edu
> Asunto: st: RE: Instrumental variables and panel data
> 
> Jaime,
> 
> > -----Original Message-----
> > From: owner-statalist@hsphsun2.harvard.edu
> > [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of 
> Jaime Gómez
> > Sent: 06 October 2009 23:13
> > To: statalist@hsphsun2.harvard.edu
> > 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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