Re: Antwort: st: difficulty in explaining GMM sargan overid

[B B <binta.sarat@yahoo.co.uk>]

Dear Johannes

Thank you very much for this. I would have a look at the literature you pointed out as Ive been reading a couple, just didnt understand them. Im glad you pointed out the large N small T as this means it might not be a good use in my project then as ive got small N size.

Anyways, thanks for this. If more help needed I wont hesitate to contact the statalist.

Cheers

Binta

--- On Thu, 24/6/10, Johannes Geyer <JGeyer@diw.de> wrote:

> From: Johannes Geyer <JGeyer@diw.de>
> Subject: Antwort: st: difficulty in explaining GMM sargan overid
> To: statalist@hsphsun2.harvard.edu
> Date: Thursday, 24 June, 2010, 10:25
> 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
> 
> 
> owner-statalist@hsphsun2.harvard.edu
> 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
> > 
> > 
> > 
> > 
> > 
> > *
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> > *   http://www.ats.ucla.edu/stat/stata/
> 
> 
> *
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