Steve,
> -----Original Message-----
> From: Steven Archambault [mailto:[email protected]]
> Sent: 14 August 2009 00:05
> To: Schaffer, Mark E
> Cc: [email protected]; [email protected];
> [email protected]
> Subject: Re: st: RE: Sargen-Hansen and instruments--RE vs. FE--Robust
>
> Thanks, but I think you misunderstood my question. I would
> like to analyze the data with robust standard errors.
I knew exactly what you wanted. My solution is this:
xtivreg dep `varlist1', re
xtoverid, robust noi
Cheers,
Mark
>
> This works,
>
> xtivreg2 dep `varlist1', fe robust;
>
> But this does not,
>
> xtivreg2 dep `varlist1', re robust;
>
> I suppose this question is a bit out of the scope of the
> original subject, but it is definitely related.
>
> Thanks!
>
> -Steve
>
>
>
> On Thu, Aug 13, 2009 at 4:57 PM, Schaffer, Mark
> E<[email protected]> wrote:
> > Steve,
> >
> >> -----Original Message-----
> >> From: Steven Archambault [mailto:[email protected]]
> >> Sent: 13 August 2009 23:48
> >> To: [email protected]
> >> Cc: [email protected]; [email protected]; Schaffer,
> >> Mark E
> >> Subject: Re: st: RE: Sargen-Hansen and instruments--RE vs.
> FE--Robust
> >>
> >> Is there a way to analyze instrumented panel data using random
> >> effects and robust standard errors? It seems the current programs
> >> don't allows this.
> >
> > You can used -xtoverid- to do this. To get an overid stat
> after -xtivreg- with random effects, -xtoverid- reestimates
> everything internally, and if you ask for a robust overid
> stat, that means it reestimates internally with robust SEs.
> >
> > If you add the option -noi- (for "noisily") to -xtoverid-
> after your estimation, you can see the results of the
> internal reestimation of the random effects model.
> >
> > The only problem is ... the variable names in the
> -xtoverid- output will all be Stata internal macros with
> names like __0000001 and so forth. You can tell which is
> which by matching the values of the coefficients in the
> -xtoverid- output to the values in the output from your
> original estimation. A bit of a hassle but it should work.
> >
> > Hope this helps.
> >
> > Cheers,
> > Mark
> >
> >> On Wed, Aug 12, 2009 at 10:28 AM, Steven
> >> Archambault<[email protected]> wrote:
> >> > Mark,
> >> >
> >> > Many thanks for your response, this clears up several
> >> questions. Yes,
> >> > I meant having a chi sq value that accepts the null that
> >> there is no
> >> > difference between RE and FE coefficients, implying the
> >> efficient RE
> >> > model is preferred.
> >> >
> >> > -Steve
> >> >
> >> >> On Wed, Aug 12, 2009 at 6:44 AM, Schaffer, Mark E
> >> <[email protected]> wrote:
> >> >>>
> >> >>> Steve,
> >> >>>
> >> >>> I'm not sure exactly what you mean in your question. For
> >> one thing,
> >> >>> rejection of the null means rejection of RE in favour
> of FE. But
> >> >>> assuming that's just a typo, here's an attempt at a
> >> restatement of
> >> >>> the question and an answer:
> >> >>>
> >> >>> 1. The difference between FE and RE can be stated in GMM
> >> terms (see
> >> >>> Hayashi's "Econometrics" for a good exposition). The FE
> >> estimator
> >> >>> uses only the orthogonality conditions that say the demeaned
> >> >>> regressor X is orthogonal to the idiosyncratic term
> e_ij. The RE
> >> >>> estimator uses these orthogonality conditions, plus the
> >> >>> orthogonality conditions that say that the mean of X for
> >> the panel
> >> >>> unit is orthogonaly to the panel error term u_j.
> >> >>>
> >> >>> 2. This is why the FE vs RE test is an overid test. The RE
> >> >>> estimator uses more orthogonality conditions, and so the
> >> equation is
> >> >>> overidentified. In the special case of classical iid
> errors, the
> >> >>> Hausman test is numerically the same as the Sargan-Hansen test.
> >> >>>
> >> >>> 3. Your question is, what happens if some of the Xs are
> >> endogenous
> >> >>> and you have some Zs as instruments? The answer is
> that the same
> >> >>> GMM framework encompasses this. You remove some of the
> >> demeaned Xs
> >> >>> from the orthogonality conditions and add some
> demeaned Zs to the
> >> >>> orthogonality conditions, and if you are using an RE
> >> estimator, you
> >> >>> also remove the panel unit means of the Xs from the
> orthogonality
> >> >>> conditions and add some panel unit means of Zs to
> them. (This is
> >> >>> the case for the EC2SLS RE estimator - it's a bit
> >> different for the
> >> >>> G2SLS estimator. The reason is that the G2SLS using a single
> >> >>> quasi-demeaned instrument Z instead of the demeaned Z and
> >> panel unit
> >> >>> mean Z separately, which is what EC2SLS does. I think
> >> the intuition
> >> >>> for EC2SLS is easier to get.)
> >> >>>
> >> >>> 4. If the FE model is overidentified, you'll now have
> an overid
> >> >>> test stat for it that tests the validity of the demeaned
> >> Zs as instruments.
> >> >>> If you're estimating an RE model, the overid test will
> test the
> >> >>> validity of the demeaned and panel unit means of the Zs
> >> and also the
> >> >>> panel unit means of the exogenous Xs.
> >> >>>
> >> >>> 5. If the overid test with endogenous regressors
> rejects the RE
> >> >>> model, you have a standard GMM problem: which of your
> >> orthogonality
> >> >>> conditions is invalid? It could be the demeaned Zs,
> or the panel
> >> >>> unit means of the Xs, or both, or whatever. In that
> >> case, you can
> >> >>> do a "GMM distance test" (aka "C test",
> >> "Difference-in-Sargan test",
> >> >>> etc.) where you compare the Sargan-Hansen test stat (from
> >> >>> -xtoverid-) after estimation with and without the orthognality
> >> >>> conditions that you think are the likely culprits. But
> >> you have to
> >> >>> decide ex ante which are the dubious ones - econometric
> >> theory can't tell you.
> >> >>>
> >> >>> Hope this helps.
> >> >>>
> >> >>> Yours,
> >> >>> Mark
> >> >>>
> >> >>> Prof. Mark Schaffer FRSE
> >> >>> Director, CERT
> >> >>> 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
> >> >>>
> >> >>>
> >> >>>
> >> >>>
> >> >>>
> >> >>> ________________________________
> >> >>>
> >> >>> From: Steven Archambault [mailto:[email protected]]
> >> >>> Sent: 12 August 2009 08:50
> >> >>> To: [email protected]; Schaffer, Mark E
> >> >>> Cc: [email protected]; [email protected]
> >> >>> Subject: Sargen-Hansen and instruments--RE vs. FE
> >> >>>
> >> >>>
> >> >>> A while back we discussed the use of the
> >> Sargen-Hansen test
> >> >>> to check if RE was an appropriate analysis to use for
> >> panel data. My
> >> >>> question now is regarding suspected endogeneity
> problems. If the
> >> >>> Sargen-Hansen statistic is such that you reject the null,
> >> in favor
> >> >>> of using the RE, does it follow that we do not need to
> >> worry about
> >> >>> explanatory variables being endogenous? My feeling is
> >> yes, here is
> >> >>> the logic. If I were to use xtivreg I would call the same over
> >> >>> identification test to see if my instruments are valid.
> >> So, if the
> >> >>> test already rejects before adding instruments, I
> should not need
> >> >>> the instruments.
> >> >>>
> >> >>> If I do use instruments, what is then a valid test to
> >> >>> determine if RE is an appropriate model to use (over FE)?
> >> >>>
> >> >>> Is my question clear?
> >> >>>
> >> >>> Thanks,
> >> >>> Steve
> >> >>>
> >> >>>
> >> >>>
> >> >>> On Sat, Jun 27, 2009 at 11:31 AM, Schaffer, Mark E
> >> >>> <[email protected]> wrote:
> >> >>>
> >> >>>
> >> >>> Steve,
> >> >>>
> >> >>> > -----Original Message-----
> >> >>> > From: [email protected]
> >> >>> >
> [mailto:[email protected]] On
> >> >>> Behalf Of
> >> >>> > Steven Archambault
> >> >>> > Sent: 27 June 2009 00:26
> >> >>> > To: [email protected];
> >> >>> [email protected];
> >> >>> > [email protected]
> >> >>> > Subject: st: Hausman test for clustered
> >> random vs.
> >> >>> fixed
> >> >>> > effects (again)
> >> >>> >
> >> >>> > Hi all,
> >> >>> >
> >> >>> > I know this has been discussed before,
> >> but in STATA
> >> >>> 10 (and
> >> >>> > versions before 9 I understand) the canned
> >> >>> procedure for
> >> >>> > Hausman test when comparing FE and RE
> >> models cannot
> >> >>> be run
> >> >>> > when the data analysis uses
> clustering (and by
> >> >>> default
> >> >>> > corrects for robust errors in STATA 10).
> >> >>> > This is the error received
> >> >>> >
> >> >>> > "hausman cannot be used with vce(robust),
> >> >>> vce(cluster cvar),
> >> >>> > or p-weighted data"
> >> >>> >
> >> >>> > My question is whether or not the
> >> approach of using
> >> >>> xtoverid
> >> >>> > to compare FE and RE models (analyzed
> using the
> >> >>> clustered and
> >> >>> > by default robust approach in STATA 10)
> >> is accepted
> >> >>> in the
> >> >>> > literature. This approach produces the
> >> >>> Sargan-Hansen stat,
> >> >>> > which is typically used with analyses
> that have
> >> >>> > instrumentalized variables and need an
> >> >>> overidentification
> >> >>> > test. For the sake of publishing I am
> >> wondering if
> >> >>> it is
> >> >>> > better just not to worry about
> >> heteroskedaticity,
> >> >>> and avoid
> >> >>> > clustering in the first place (even though
> >> >>> heteroskedaticity
> >> >>> > likely exists)? Or, alternatively one
> could just
> >> >>> calculate
> >> >>> > the Hausman test by hand following
> the clustered
> >> >>> analyses.
> >> >>> >
> >> >>> > Thanks for your insight.
> >> >>>
> >> >>> It's very much accepted in the
> literature. In the
> >> >>> -xtoverid- help file,
> >> >>> see especially the paper by Arellano and
> >> the book by
> >> >>> Hayashi.
> >> >>>
> >> >>> If you suspect heteroskedasticity or clustered
> >> >>> errors, there really is
> >> >>> no good reason to go with a test
> (classic Hausman)
> >> >>> that is invalid in
> >> >>> the presence of these problems. The GMM
> >> -xtoverid-
> >> >>> approach is a
> >> >>> generalization of the Hausman test, in the
> >> following
> >> >>> sense:
> >> >>>
> >> >>> - The Hausman and GMM tests of fixed vs. random
> >> >>> effects have the same
> >> >>> degrees of freedom. This means the result
> >> cited by
> >> >>> Hayashi (and due to
> >> >>> Newey, if I recall) kicks in, namely...
> >> >>>
> >> >>> - Under the assumption of homoskedasticity and
> >> >>> independent errors, the
> >> >>> Hausman and GMM test statistics are numerically
> >> >>> identical. Same test.
> >> >>>
> >> >>> - When you loosen the iid assumption and allow
> >> >>> heteroskedasticity or
> >> >>> dependent data, the robust GMM test is
> the natural
> >> >>> generalization.
> >> >>>
> >> >>> Hope this helps.
> >> >>>
> >> >>> Cheers,
> >> >>> Mark (author of -xtoverid-)
> >> >>>
> >> >>> > *
> >> >>> > * 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/
> >> >>> >
> >> >>>
> >> >>>
> >> >>> --
> >> >>> 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/
> >> >>>
> >> >>>
> >> >>>
> >> >>>
> >> >>>
> >> >>> --
> >> >>> 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/
> >> >>
> >> >
> >>
> >
> >
> > --
> > Heriot-Watt University is a Scottish charity registered
> under charity
> > number SC000278.
> >
> >
>
--
Heriot-Watt University is a Scottish charity
registered under charity number SC000278.
*
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