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From |
Johannes Geyer <JGeyer@diw.de> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Antwort: st: difficulty in explaining GMM sargan overid |

Date |
Thu, 24 Jun 2010 11:52:20 +0200 |

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/

**Follow-Ups**:**Re: Antwort: st: difficulty in explaining GMM sargan overid***From:*B B <binta.sarat@yahoo.co.uk>

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

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