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st: difficulty in explaining GMM sargan overid


From   B B <binta.sarat@yahoo.co.uk>
To   statalist@hsphsun2.harvard.edu
Subject   st: difficulty in explaining GMM sargan overid
Date   Wed, 23 Jun 2010 17:42:55 +0000 (GMT)

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	



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