# st: ivreg2 2sls, gmm2s and autocorrelation test

 From Marie-Hélène Felt To statalist@hsphsun2.harvard.edu Subject st: ivreg2 2sls, gmm2s and autocorrelation test Date Tue, 21 Oct 2008 12:13:36 -0400

```hello,

I'm using IVREG2 to estimate a regression with one endogenous regressor.
I noticed that the results of -abar- (test for AC) are really different after a
2SLS H robust estimation and a GMM2S H robust estimation. After 2SLS it seems
that I have AC, but not after GMM2S...but it's the same equation I'm
estimating!!

I'm working with time series, and not with cross sectional time series, so I'm
wondering if I'm allowed to use -abar- after both ivreg2 estimations (2sls and
gmm2s).
If indeed I'm allowed to use it, how should I understand these results?
Would you suggest to use -ivactest- rather than -abar-??

I report hereafter my results.

Marie Helene

. ivreg2 lnpda lntxus lnpvus lntxca lnpvca lnipja (lnqda=lntxda lnpvda), robust

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

Number of obs =      148
F(  6,   141) =     4.96
Prob > F      =   0.0001
Total (centered) SS     =  1.439041186                Centered R2   =  -0.0540
Total (uncentered) SS   =  26084.88825                Uncentered R2 =   0.9999
Residual SS             =  1.516815821                Root MSE      =    .1012

------------------------------------------------------------------------------
|               Robust
lnpda |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
lnqda |  -.0943358   .0205959    -4.58   0.000    -.1347031   -.0539685
lntxus |   .3162592   .3225253     0.98   0.327    -.3158787    .9483971
lnpvus |   .1865913   .2975095     0.63   0.531    -.3965166    .7696993
lntxca |  -.2562668   .3309884    -0.77   0.439    -.9049921    .3924585
lnpvca |  -.1924842   .3003962    -0.64   0.522    -.7812501    .3962816
lnipja |   .2326394   .2220086     1.05   0.295    -.2024894    .6677681
_cons |    12.7047   1.322835     9.60   0.000     10.11199    15.29741
------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):             13.650
Chi-sq(2) P-val =    0.0011
------------------------------------------------------------------------------
Weak identification test (Kleibergen-Paap rk Wald F statistic):          8.977
Stock-Yogo weak ID test critical values: 10% maximal IV size             19.93
15% maximal IV size             11.59
20% maximal IV size              8.75
25% maximal IV size              7.25
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):         0.533
Chi-sq(1) P-val =    0.4653
------------------------------------------------------------------------------
Instrumented:         lnqda
Included instruments: lntxus lnpvus lntxca lnpvca lnipja
Excluded instruments: lntxda lnpvda
------------------------------------------------------------------------------

. abar, lags(6)
Warning: The Arellano-Bond test is only valid for time series only if they are
ergodic.
Arellano-Bond test for AR(1): z =   5.77  Pr > z = 0.0000
Arellano-Bond test for AR(2): z =   4.34  Pr > z = 0.0000
Arellano-Bond test for AR(3): z =   3.48  Pr > z = 0.0005
Arellano-Bond test for AR(4): z =   2.02  Pr > z = 0.0437
Arellano-Bond test for AR(5): z =   0.47  Pr > z = 0.6380
Arellano-Bond test for AR(6): z =   0.96  Pr > z = 0.3350

. ivreg2 lnpda lntxus lnpvus lntxca lnpvca lnipja (lnqda=lntxda lnpvda), gmm2s
robust

2-Step GMM estimation
---------------------

Estimates efficient for arbitrary heteroskedasticity
Statistics robust to heteroskedasticity

Number of obs =      148
F(  6,   141) =     4.89
Prob > F      =   0.0001
Total (centered) SS     =  1.439041186                Centered R2   =  -0.0525
Total (uncentered) SS   =  26084.88825                Uncentered R2 =   0.9999
Residual SS             =  1.514566079                Root MSE      =    .1012

------------------------------------------------------------------------------
|               Robust
lnpda |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
lnqda |  -.0941792   .0205948    -4.57   0.000    -.1345443   -.0538141
lntxus |   .3132924   .3224997     0.97   0.331    -.3187954    .9453801
lnpvus |   .1590178   .2951029     0.54   0.590    -.4193732    .7374088
lntxca |  -.2397403   .3302135    -0.73   0.468    -.8869469    .4074662
lnpvca |   -.163539   .2977688    -0.55   0.583    -.7471551    .4200772
lnipja |   .2394675   .2218115     1.08   0.280    -.1952751    .6742101
_cons |   12.61117   1.316618     9.58   0.000     10.03065    15.19169
------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):             13.650
Chi-sq(2) P-val =    0.0011
------------------------------------------------------------------------------
Weak identification test (Kleibergen-Paap rk Wald F statistic):          8.977
Stock-Yogo weak ID test critical values: 10% maximal IV size             19.93
15% maximal IV size             11.59
20% maximal IV size              8.75
25% maximal IV size              7.25
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):         0.533
Chi-sq(1) P-val =    0.4653
------------------------------------------------------------------------------
Instrumented:         lnqda
Included instruments: lntxus lnpvus lntxca lnpvca lnipja
Excluded instruments: lntxda lnpvda
------------------------------------------------------------------------------

. abar, lags(6)
Warning: The Arellano-Bond test is only valid for time series only if they are
ergodic.
Arellano-Bond test for AR(1): z =   0.99  Pr > z = 0.3202
Arellano-Bond test for AR(2): z =   0.99  Pr > z = 0.3203
Arellano-Bond test for AR(3): z =   0.99  Pr > z = 0.3232
Arellano-Bond test for AR(4): z =   0.99  Pr > z = 0.3233
Arellano-Bond test for AR(5): z =   0.83  Pr > z = 0.4050
Arellano-Bond test for AR(6): z =   0.85  Pr > z = 0.3944

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```