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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 14:23:42 +0000 (GMT) |

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 > > > > > > > > > > > > * > > * 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/

**References**:**Antwort: st: difficulty in explaining GMM sargan overid***From:*Johannes Geyer <JGeyer@diw.de>

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