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Re: st: RE: Sargen-Hansen and instruments--RE vs. FE--Robust


From   Marco Buur <marco.buur@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: RE: Sargen-Hansen and instruments--RE vs. FE--Robust
Date   Wed, 19 Aug 2009 12:22:28 +0200

Dear All
I am wondering if xttest3 checks heteroskedasticity in fixed effect
regression model between the clusters or it is better to use xtcltest
or cltest. I tiried to run xtcltest or cltest (Stata) 10 it didn't
work.
Any hint?

xtreg , fe
xttest3

Marco


On Tue, Aug 18, 2009 at 5:28 PM, Steven Archambault<archstevej@gmail.com> wrote:
> On Tue, Aug 18, 2009 at 9:27 AM, Steven Archambault<archstevej@gmail.com> wrote:
>> This approach is working. I was hoping to calculate the Cragg Donald
>> Wald by hand, but it seems I cannot get the  "minimum eigenvalue of
>> the Cragg-Donald" statistic from ereturn. Would this be something I
>> could calculate another way myself?
>>
>>
>>
>>
>>
>>
>> On Thu, Aug 13, 2009 at 4:57 PM, Schaffer, Mark E<M.E.Schaffer@hw.ac.uk> wrote:
>>> Steve,
>>>
>>>> -----Original Message-----
>>>> From: Steven Archambault [mailto:archstevej@gmail.com]
>>>> Sent: 13 August 2009 23:48
>>>> To: statalist@hsphsun2.harvard.edu
>>>> Cc: austinnichols@gmail.com; Alfred.Stiglbauer@oenb.at;
>>>> 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<archstevej@gmail.com> 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
>>>> <M.E.Schaffer@hw.ac.uk> 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:archstevej@gmail.com]
>>>> >>>        Sent: 12 August 2009 08:50
>>>> >>>        To: statalist@hsphsun2.harvard.edu; Schaffer, Mark E
>>>> >>>        Cc: austinnichols@gmail.com; Alfred.Stiglbauer@oenb.at
>>>> >>>        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
>>>> >>> <M.E.Schaffer@hw.ac.uk> wrote:
>>>> >>>
>>>> >>>
>>>> >>>                Steve,
>>>> >>>
>>>> >>>                > -----Original Message-----
>>>> >>>                > From: owner-statalist@hsphsun2.harvard.edu
>>>> >>>                > [mailto:owner-statalist@hsphsun2.harvard.edu] On
>>>> >>> Behalf Of
>>>> >>>                > Steven Archambault
>>>> >>>                > Sent: 27 June 2009 00:26
>>>> >>>                > To: statalist@hsphsun2.harvard.edu;
>>>> >>> austinnichols@gmail.com;
>>>> >>>                > Alfred.Stiglbauer@oenb.at
>>>> >>>                > 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-)
>>>> >>>
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