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Re: Antwort: st: difficulty in explaining GMM sargan overid
From
B B <[email protected]>
To
[email protected]
Subject
Re: Antwort: st: difficulty in explaining GMM sargan overid
Date
Thu, 24 Jun 2010 14:26:28 +0000 (GMT)
Ha!
I think I should have read both the post before replying. I mean when I read the archives, it was pointed out that having a ch2(98) I think, was too large and my chi2 (344) was that...
Anyways, I'll have a read through the papers and also use the Hensen's J test as suggested.
Binta
--- On Thu, 24/6/10, Johannes Geyer <[email protected]> wrote:
> From: Johannes Geyer <[email protected]>
> Subject: Antwort: st: difficulty in explaining GMM sargan overid
> To: [email protected]
> Date: Thursday, 24 June, 2010, 10:52
> Sorry, just a quick add to my
> previous post:
>
> "too large" means that the Sargan test statistic tends to
> get "weaker" if
> there are many instruments as in your case.
> That means, it does not reject often enough your
> instruments. You could
> simply reduce the lags used as
> instruments and see whether the test is robust to this
> excercise. But note
> also that the Sargan test statistic is
> not robust to heteroskedasticity - check if you can run the
> robust version
> of this test, the Hansen of J test.
>
> Johannes
>
>
>
> [email protected]
> schrieb am 24/06/2010 11:25:33:
>
> > Dear Binta,
> >
> > I don't know what it means if your chi() is "too
> large". I would
> interpret
> > the test results as you did.
> > Note that these models were developed for large N and
> small T.
> >
> > A good starting point to learn these dynamic GMM
> models for applied
> > research is
> >
> > http://www.cemmap.ac.uk/wps/cwp0209.pdf
> >
> > and David Roodman, the auther of the Stata-ado command
> -xtabond2- wrote
> a
> > very good introduction too:
> >
> > http://ideas.repec.org/p/boc/asug06/8.html
> >
> > If you cite other studies, you should provide the full
> reference. Here
> is
> > a quote from the Statalist FAQs
> >
> > http://www.stata.com/support/faqs/res/statalist.html
> >
> > Precise literature references please! Please do not
> assume that the
> > literature familiar to you is familiar to all members
> of Statalist. Do
> not
> > refer to publications with just minimal details (e.g.,
> author and date).
>
> > Questions of the form ?Has anyone implemented the
> heteroscedasticity
> under
> > a full moon test of Sue, Grabbit, and Runne (1989)??
> admittedly divide
> the
> > world. Anyone who has not heard of the said test would
> not be helped by
> > the full reference to answer the question, but they
> might well
> appreciate
> > the full reference.
> >
> > Hope this helps,
> >
> > Johannes
> >
> >
> > [email protected]
> schrieb am 23/06/2010 19:42:55:
> >
> > > Dear All,
> > >
> > > I am kind of new to the GMM procedure and like a
> newbie, I am having
> > > difficulties understanding the main intution
> behind it. My main
> > > purpose of using GMM is to enable me deal with
> endogeneity problem
> > > which may arise in the analysis I intend to carry
> out. In my
> > > research, I want to examine the impact of
> financial liberalisation
> > > on financial development in emerging countries.
> > >
> > > My sample consists of 11 countries over 28 years
> which gives a total
> > > of 308 obs. However, reading through some of the
> archives, I noticed
> > > that my chi2(344) might be too big and probably
> create a problem. I
> > > might be wrong but like earlier stated, I am a
> novice in this.
> > >
> > > My depvar is FD for both bank and stock
> marketindvar includes
> > > lnpcap, bhldate, trade, infl, fdi and
> institutions. To test the RZ
> > > hypothesis I have included the interactions
> between FO and TO. My
> > > model is similar to that of Baltagi et al (2007)
> and Ito (2006).
> > > From what I understand, you would have to include
> the lag dependent
> > > variable and lag of the indvar as instruments in
> the GMM estimation,
> > > correct me if Im wrong.
> > > My main problem now is, using the xtabond command
> in stata 9, I
> > > obtained the following:
> > >
> > > Arellano-Bond dynamic panel-data estimation
> Number of obs =
> > > 209Group variable (i): cty
>
> Number of groups =
> > 11
> > > Wald chi2(7)
> = 1008.11
> > > Time variable (t): year
>
> Obs per group: min =
> > > 11avg = 19max
> = 23
> > > One-step results
> > > D.m3wdi
> Coef. Std. Err.
> z P>z
> [95% Conf.
> > > Interval] m3wdi LD.
> .8884923 .047715
> 18.62 0.000 .
> > > 7949727 .9820119bhldate
> D1. 1.453598 1.312559
> 1.11 0.
> > > 268
> -1.118971 4.026166lnpcapwdi D1.
> 2.620653 3.494215
> > > 0.75 0.453
> -4.227882 9.469188trade D1.
> .0624551 .0328946
> > >
> 1.90 0.058
> -.0020171 .1269274inf
> D1. -.0914649 .
> > > 0278294
> -3.29 0.001
> -.1460095 -.0369202fdi D1.
> .2869984
> > > .2403093
> 1.19 0.232
> -.1839991 .757996icrgqog
> D1. -9.
> > > 449567 4.480311
> -2.11 0.035
> -18.23082 -.6683196_cons
> > > -.101707 .1329192
> -0.77 0.444
> -.3622238 .1588098
> > >
> > > Sargan test of over-identifying
> restrictions: chi2(344)
> = 193.
> > > 65 Prob > chi2 =
> 1.0000
> > >
> > > Arellano-Bond test that average autocovariance in
> residuals of order
> > > 1 is 0:H0: no autocorrelation z
> = -7.08 Pr > z = 0.0000
> > >
> > > Arellano-Bond test that average autocovariance in
> residuals of order
> > > 2 is 0:H0: no autocorrelation z
> = 0.56 Pr > z =
> 0.577538
> .1588098
> >
> > >
> > > From my understanding of the sargan test, the
> chi2(344) = 1.0000
> > > should mean that I cannot reject the
> overidentifying restrictions.
> > > However, like I stated earlier, according to the
> archives, my
> > > chi2(344) might be too large, but I dont think I
> understand this
> > > reason, I am confused or maybe confusing myself
> > > I indeed will appreciate any help to clarify
> this.
> > >
> > > Thanks
> > > Binta
> > >
> > >
> > >
> > >
> > >
> > > *
> > > * For searches and help try:
> > > * http://www.stata.com/help.cgi?search
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> > > * http://www.ats.ucla.edu/stat/stata/
> >
> >
> > *
> > * For searches and help try:
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> > * http://www.stata.com/support/statalist/faq
> > * http://www.ats.ucla.edu/stat/stata/
>
> *
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>
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