Bookmark and Share

Notice: On March 31, it was announced that Statalist is moving from an email list to a forum. The old list will shut down at the end of May, and its replacement, statalist.org is already up and running.


[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

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


From   B B <binta.sarat@yahoo.co.uk>
To   statalist@hsphsun2.harvard.edu
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 <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, 10:52
> 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
> 
> 
> 
> owner-statalist@hsphsun2.harvard.edu
> 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
> > 
> > 
> > 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
> > > 
> > > 
> > > 
> > > 
> > > 
> > > *
> > > *   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/
> > 
> > 
> > *
> > *   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/
> 
> *
> *   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/
> 


      

*
*   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/


© Copyright 1996–2014 StataCorp LP   |   Terms of use   |   Privacy   |   Contact us   |   Site index