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Re: st: How can I interpret the result of xtabond2 ?


From   Johannes Geyer <JGeyer@diw.de>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: How can I interpret the result of xtabond2 ?
Date   Tue, 23 Sep 2008 14:17:34 +0200

the -help- for xtabond2 is quiet comprehensive

these papers might be helpful:

http://ideas.repec.org/p/cgd/wpaper/103.html
www.nuff.ox.ac.uk/users/bond/cwp0209.pdf

Regards,
Johannes


----------------------
Johannes Geyer
Deutsches Institut für Wirtschaftsforschung (DIW Berlin)
German Institute for Economic Research 
Department of Public Economics
DIW Berlin
Mohrenstraße 58
10117 Berlin
Tel: +49-30-89789-258

owner-statalist@hsphsun2.harvard.edu schrieb am 23/09/2008 06:18:15:

> Dear stata users
> 
> I'm a just beginner for xtabond2, so I don't know how to interpret 
> the result below especially regarding
> 
> Arellano-Bond autocorrelation test, Sargan test and Hansen test.
> 
> I'm not sure but my guess is that it says that there is both AR(1) 
> and AR(2), may be some IVs are correlated with error term.  If my 
> guess is right, then what should I do?
> 
> Please see my log below and help!!
> 
> 
> 
> . xtabond2 dlcapx l1_dlcapx dls l1_xinter4 l1_minter4 y9* y0*, 
> gmm(l1_dlcapx dls) iv(l1_xinter4 l1_minter4 y9* y0*, equation(level)) 
orth
> > ogonal robust twostep
> Favoring speed over space. To switch, type or click on mata: mata 
> set matafavor space, perm.
> y90 dropped due to collinearity
> y91 dropped due to collinearity
> y92 dropped due to collinearity
> y93 dropped due to collinearity
> y94 dropped due to collinearity
> y95 dropped due to collinearity
> y96 dropped due to collinearity
> y97 dropped due to collinearity
> y07 dropped due to collinearity
> 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 statistics may be negative.
> Dynamic panel-data estimation, two-step system GMM
> 
------------------------------------------------------------------------------
> Group variable: gvkey                           Number of obs      = 
3607
> Time variable : year                            Number of groups   =  
874
> Number of instruments = 218                     Obs per group: min =  2
> Wald chi2(13) =    211.52                                      avg = 
4.13
> Prob > chi2   =     0.000                                      max =  10
> 
------------------------------------------------------------------------------
>              |              Corrected
>       dlcapx |      Coef.   Std. Err.      z    P>|z|     [95% 
Conf.Interval]
> 
-------------+----------------------------------------------------------------
>    l1_dlcapx |  -.2007464   .0302184    -6.64   0.000    -.2599734 
-.1415195
>          dls |   .6778161   .0993139     6.82   0.000     .4831644 
.8724677
>   l1_xinter4 |   -2.53827    1.47796    -1.72   0.086     -5.43502 
.3584791
>   l1_minter4 |   17.70894   5.754314     3.08   0.002     6.430696 
28.98719
>          y98 |  -.0185858   .2891494    -0.06   0.949    -.5853082 
.5481365
>          y99 |  -.0792788   .2839134    -0.28   0.780    -.6357388 
.4771813
>          y00 |  -.0249017   .2810219    -0.09   0.929    -.5756945 
.5258911
>          y01 |  -.2103614   .2918474    -0.72   0.471    -.7823718 
.361649
>          y02 |  -.2527464   .2846071    -0.89   0.375    -.8105661 
.3050734
>          y03 |  -.0863709   .2797089    -0.31   0.757    -.6345903 
.4618485
>          y04 |   .2565646   .2892443     0.89   0.375    -.3103438 
.8234729
>          y05 |   .1678996   .2790313     0.60   0.547    -.3789918 
.7147909
>          y06 |   .0993331   .2821207     0.35   0.725    -.4536133 
.6522794
>        _cons |  -.0553731    .277753    -0.20   0.842     -.599759 
.4890129
> 
------------------------------------------------------------------------------
> Instruments for orthogonal deviations equation
>   GMM-type (missing=0, separate instruments for each period unless 
collapsed)
>     L(1/.).(l1_dlcapx dls)
> Instruments for levels equation
>   Standard
>     _cons
>     l1_xinter4 l1_minter4 y90 y91 y92 y93 y94 y95 y96 y97 y98 y99 y00 
y01 y02
>     y03 y04 y05 y06 y07
>   GMM-type (missing=0, separate instruments for each period unless 
collapsed)
>     D.(l1_dlcapx dls)
> 
------------------------------------------------------------------------------
> Arellano-Bond test for AR(1) in first differences: z =  -6.72  Pr > z = 
0.000
> Arellano-Bond test for AR(2) in first differences: z =  -3.23  Pr > z = 
0.001
> 
------------------------------------------------------------------------------
> Sargan test of overid. restrictions: chi2(204)  = 621.40  Prob > chi2 = 
0.000
>   (Not robust, but not weakened by many instruments.)
> Hansen test of overid. restrictions: chi2(204)  = 238.63  Prob > chi2 = 
0.049
>   (Robust, but can be weakened by many instruments.)
> Difference-in-Hansen tests of exogeneity of instrument subsets:
>   GMM instruments for levels
>     Hansen test excluding group:     chi2(184)  = 216.29  Prob > chi2 = 
0.052
>     Difference (null H = exogenous): chi2(20)   =  22.35  Prob > chi2 = 
0.322
>   iv(l1_xinter4 l1_minter4 y90 y91 y92 y93 y94 y95 y96 y97 y98 y99 
> y00 y01 y02 y03 y04 y05 y06 y07, eq(level))
>     Hansen test excluding group:     chi2(193)  = 227.63  Prob > chi2 = 
0.045
>     Difference (null H = exogenous): chi2(11)   =  11.00  Prob > chi2 = 
0.443
> 
> 
> I really appreciate your cooperation!!
> 
> Caleb
> 
> 
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