Dear Statalist,


I am using stata v.14... I have unbalanced panel data with T = 17 and N = 18. I mostly have reversal causality from 2 of my control variables (z1 and z2 )and my main independent variable (M1). When I run two-step system GMM, the results change dramatically, eroding the significance of most, if not all, the variables. However, the model is valid according to AR(2), sargan , and Hansan tests, as follows:
Code:

Code:
xtabond2 lny l.lny lnz1 x1 x2 x3 x4 x5 m1 m1sq m1_z2 z2 x6 x7, ///
gmm (l.lny , lag(1 3) collapse) iv ( lnz1 x1 x2 x3 x4 x5 m1 m1sq m1_z2 z2 x6 x7 ) twostep cluster(country) nodiffsargan



Dynamic panel-data estimation, two-step system GMM
------------------------------------------------------------------------------
Group variable: country                    Number of obs = 177
Time variable : time                          Number of groups = 17
Number of instruments = 17            Obs per group: min = 1
Wald chi2(13) = 1.40e+10               avg = 10.41
Prob > chi2 = 0.000                           max = 16
(Std. Err. adjusted for clustering on country)
------------------------------------------------------------------------------
| Corrected
lny |             Coef.     Std. Err.      z         P>|z|       [95% Conf. Interval]
-------------+----------------------------------------------------------------
lny|
L1. |        1.041343   .0277953  37.46    0.000      .9868654  1.095821
|
lnz1 |      .0005234   .0033601    0.16    0.876    -.0060623 .007109
x1|          .0000164   .0000536    0.31   0.760    -.0000887 .0001214
x2|          .0000259   .0001718    0.15   0.880    -.0003108 .0003625
x3|          .0002729   .0005714    0.48   0.633    -.0008469 .0013927
x4|         -.0000124   .0000428   -0.29   0.772    -.0000962 .0000714
x5|         -.0001993   .0002734   -0.73   0.466    -.0007351 .0003365
m1|         .0052374   .0120197    0.44    0.663    -.0183208 .0287955
m1sq |   -.0018499   .0028094   -0.66      0.510    -.0073563 .0036564
m1_z2 | -.0002276   .0034992   -0.07       0.948    -.0070859 .0066308
z2|         -.0019458   .0062422   -0.31   0.755    -.0141803 .0102886
x6|         -.002183     .0010165   -2.15  0.032    -.0041753 -.0001908
x7|          .0000406   .0000986    0.4    0.680    -.0001525 .0002338
_cons | -.1274088   .1413448    -0.90      0.367    -.4044396 .149622
------------------------------------------------------------------------------
Instruments for first differences equation
Standard
D.(lnz1 x1 x2 x3 x4 x5 m1 m1sq m1_z2 z2 x6 x7)
GMM-type (missing=0, separate instruments for each period unless collapsed)
L(1/3).L.lny collapsed
Instruments for levels equation
Standard
lnz1 x1 x2 x3 x4 x5 m1 m1sq m1_z2 z2 x6 x7
_cons
GMM-type (missing=0, separate instruments for each period unless collapsed)
D.L.lngini collapsed
------------------------------------------------------------------------------
Arellano-Bond test for AR(1) in first differences: z = -3.09 Pr > z = 0.002
Arellano-Bond test for AR(2) in first differences: z = -0.89 Pr > z = 0.375
------------------------------------------------------------------------------
Sargan test of overid. restrictions: chi2(3) = 0.53 Prob > chi2 = 0.913
(Not robust, but not weakened by many instruments.)
Hansen test of overid. restrictions: chi2(3) = 0.49 Prob > chi2 = 0.921
(Robust, but weakened by many instruments.)
I tried to put the endogenous variables in the gmm style part, however it does not provide much changes in the significance problem. I also tried to change the number of lags, however it is relatively useless as well. So what is the wrong in my command pls.