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


From   "Schaffer, Mark E" <M.E.Schaffer@hw.ac.uk>
To   "Steven Archambault" <archstevej@gmail.com>, <statalist@hsphsun2.harvard.edu>
Subject   st: RE: Sargen-Hansen and instruments--RE vs. FE
Date   Wed, 12 Aug 2009 13:44:42 +0100

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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registered under charity number SC000278.


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