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I would really appreciate any comments, thank you in advance. Evan M. My estimation is below: xtabond2 oss l.oss realyldglp OE2assets TE2assets glp2assets costperloan iyear lindi lsolid lvillage, gmm(oss lindi lsolid lvillage) iv(iyear l2.pm) two robust small orthogonal Favoring space over speed. To switch, type or click on mata: mata set matafavor speed, perm. Warning: Two-step estimated covariance matrix of moments is singular. Using a generalized inverse to calculate optimal weighting matrix for two-step estimation. Difference-in-Sargan/Hansen statistics may be negative. Dynamic panel-data estimation, two-step system GMM ------------------------------------------------------------------------------ Group variable: mfiid Number of obs = 379 Time variable : year Number of groups = 233 Number of instruments = 42 Obs per group: min = 0 F(10, 232) = 11.06 avg = 1.63 Prob > F = 0.000 max = 3 ------------------------------------------------------------------------------ | Corrected oss | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- oss | L1. | -.2034418 .1627875 -1.25 0.213 -.5241726 .1172889 | realyldglp | 1.554736 1.142141 1.36 0.175 -.6955579 3.805029 OE2assets | .6808866 3.31698 0.21 0.838 -5.854367 7.21614 TE2assets | -3.917173 2.493656 -1.57 0.118 -8.830279 .9959336 glp2assets | 2.215048 1.15665 1.92 0.057 -.0638324 4.493929 costperloan | -.0002647 .0025206 -0.11 0.916 -.005231 .0047015 iyear | .0948036 .0881907 1.07 0.283 -.0789535 .2685606 lindi | -.0716274 .2376581 -0.30 0.763 -.5398714 .3966167 lsolid | .0086089 .0974341 0.09 0.930 -.1833599 .2005777 lvillage | -.0022004 .0250286 -0.09 0.930 -.0515128 .0471119 _cons | .6784071 4.190431 0.16 0.872 -7.577756 8.93457 ------------------------------------------------------------------------------ Instruments for orthogonal deviations equation Standard FOD.(iyear L2.pm) GMM-type (missing=0, separate instruments for each period unless collapsed) L(1/5).(oss lindi lsolid lvillage) Instruments for levels equation Standard iyear L2.pm _cons GMM-type (missing=0, separate instruments for each period unless collapsed) D.(oss lindi lsolid lvillage) ------------------------------------------------------------------------------ Arellano-Bond test for AR(1) in first differences: z = -1.00 Pr > z = 0.315 Arellano-Bond test for AR(2) in first differences: z = 0.10 Pr > z = 0.917 ------------------------------------------------------------------------------ Sargan test of overid. restrictions: chi2(31) = 87.67 Prob > chi2 = 0.000 (Not robust, but not weakened by many instruments.) Hansen test of overid. restrictions: chi2(31) = 19.86 Prob > chi2 = 0.939 (Robust, but weakened by many instruments.) Difference-in-Hansen tests of exogeneity of instrument subsets: GMM instruments for levels Hansen test excluding group: chi2(19) = 12.54 Prob > chi2 = 0.861 Difference (null H = exogenous): chi2(12) = 7.32 Prob > chi2 = 0.836 iv(iyear L2.pm) Hansen test excluding group: chi2(29) = 16.57 Prob > chi2 = 0.968 Difference (null H = exogenous): chi2(2) = 3.29 Prob > chi2 = 0.193 On Wed, Apr 24, 2013 at 5:52 PM, Evan Markel <markelev@gmail.com> wrote: > Dear listservers, > > I am estimating an xtabond2 model using a panel where N=434 > microfinance institutions (MFI's) and where T=5. After executing > xtabond2 system GMM this reduces to N=233 and T=3. > > My primary concern right now is the implication of failing to reject > the null hypothesis of no autocorrelation in the Arellano-Bond test > for AR(1). I have read Roodman 2006 and understand that negative first > order serial correlation is to be expected in AR(1) because of the > mathematical relation between the first difference and the first lag > of difference. > > Specifically (eit)-(eit-1) is related to (eit-1)-(eit-2). But what > does this mean when I fail to reject the null. Does it give evidence > of a random walk? I have found several examples and discussions > stating why first order serial correlation is expected in AR(1), but > have not found a discussion on the implications of the contrary. > > Below is an extract from my xtabond2 output. > > Instruments for orthogonal deviations equation > Standard > FOD.(iyear L2.pm) > GMM-type (missing=0, separate instruments for each period unless collapsed) > L(1/5).(oss lindi lsolid lvillage) > Instruments for levels equation > Standard > iyear L2.pm > _cons > GMM-type (missing=0, separate instruments for each period unless collapsed) > D.(oss lindi lsolid lvillage) > ------------------------------------------------------------------------------ > Arellano-Bond test for AR(1) in first differences: z = -1.00 Pr > z = 0.315 > Arellano-Bond test for AR(2) in first differences: z = 0.10 Pr > z = 0.917 > ------------------------------------------------------------------------------ > Sargan test of overid. restrictions: chi2(31) = 87.67 Prob > chi2 = 0.000 > (Not robust, but not weakened by many instruments.) > Hansen test of overid. restrictions: chi2(31) = 19.86 Prob > chi2 = 0.939 > (Robust, but weakened by many instruments.) > > Difference-in-Hansen tests of exogeneity of instrument subsets: > GMM instruments for levels > Hansen test excluding group: chi2(19) = 12.54 Prob > chi2 = 0.861 > Difference (null H = exogenous): chi2(12) = 7.32 Prob > chi2 = 0.836 > iv(iyear L2.pm) > Hansen test excluding group: chi2(29) = 16.57 Prob > chi2 = 0.968 > Difference (null H = exogenous): chi2(2) = 3.29 Prob > chi2 = 0.193 > > My variable of interest is Operational Self-sufficiency (OSS). > Following Roodman 2009 I regress OSS on the lagged level of OSS which > yields a low R^2 of .117 possibly signally lag 1.oss is a weak > instrument. Maybe this is a source of my failure to reject AR(1)? > > regress oss l.oss > > Source | SS df MS > Number of obs = 784 > -------------+------------------------------ > F( 1, 782) = 103.65 > Model | 19.0811599 1 19.0811599 > Prob > F = 0.0000 > Residual | 143.966445 782 .184100313 > R-squared = 0.1170 > -------------+------------------------------ > Adj R-squared = 0.1159 > Total | 163.047605 783 .208234489 > Root MSE = .42907 > > ------------------------------------------------------------------------------ > oss | Coef. Std. Err. t P>|t| [95% > Conf. Interval] > -------------+---------------------------------------------------------------- > oss | > L1. | .3328396 .0326934 10.18 0.000 .2686624 .3970168 > | > _cons | .7341655 .0399425 18.38 0.000 .6557582 .8125727 > > > Thank you in advance for your help, > > Evan M. > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/faqs/resources/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/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

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