Hello,
I'm trying to study the spillover effect of trading with Arab Spring countries on economic growth for trading partner. I used two-step GMM method.
lngdp = lngdpt-1 + Trade with AS + Trade with non-AS + X
I used both synx
xtabond2 g_realgdp L.g_realgdp ln_inv polstab ln_popgrow ln_k exprt_arab2009 exprt_nonarab2009 i.y, gmm(L.g_realgdp exprt_arab2009 exprt_nonarab2009, collapse) iv(ln_inv polstab ln_popgrow ln_k exprt_arab2009 exprt_nonarab2009 i.y, equation(level)) nodiffsargan twostep robust orthogonal small

My results
Dynamic panel-data estimation, two-step system GMM
------------------------------------------------------------------------------
Group variable: id Number of obs = 1361
Time variable : years Number of groups = 106
Number of instruments = 50 Obs per group: min = 2
F(20, 105) = 96.11 avg = 12.84
Prob > F = 0.000 max = 14
-------------------------------------------------------------------------------
| Corrected
g_realgdp | Coefficient std. err. t P>|t| [95% conf. interval]
--------------+----------------------------------------------------------------
g_realgdp |
L1. | .3466934 .0799472 4.34 0.000 .1881728 .505214
|
ln_inv | .0183315 .0081626 2.25 0.027 .0021466 .0345165
polstab | -.0048779 .0012328 -3.96 0.000 -.0073224 -.0024335
ln_popgrow | .0016554 .0009214 1.80 0.075 -.0001716 .0034824
ln_k | -.0009274 .0005903 -1.57 0.119 -.0020977 .000243
exprt_ar~2009 | -.0002798 .0002997 -0.93 0.353 -.000874 .0003145
exprt_no~2009 | .0001107 .0000634 1.75 0.084 -.000015 .0002364
|
years |
2005 | .0053833 .0047511 1.13 0.260 -.0040372 .0148038
2006 | .0108478 .0041824 2.59 0.011 .0025549 .0191408
2007 | .0085987 .0041307 2.08 0.040 .0004083 .0167891
2008 | -.010197 .0052332 -1.95 0.054 -.0205735 .0001794
2009 | -.0357687 .0057003 -6.27 0.000 -.0470713 -.0244662
2010 | .0199983 .0040512 4.94 0.000 .0119655 .028031
2011 | -.0053902 .0039893 -1.35 0.180 -.0133003 .00252
2012 | -.0061589 .0040205 -1.53 0.129 -.0141309 .0018131
2013 | -.0081049 .0050861 -1.59 0.114 -.0181897 .0019798
2014 | -.0013908 .002677 -0.52 0.604 -.0066987 .0039172
2015 | -.0054758 .0027612 -1.98 0.050 -.0109508 -8.56e-07
2016 | -.0088888 .0036413 -2.44 0.016 -.0161089 -.0016688
2018 | -.0057051 .0022761 -2.51 0.014 -.0102182 -.0011919
|
_cons | -.0040318 .0293484 -0.14 0.891 -.0622242 .0541606
-------------------------------------------------------------------------------
Instruments for orthogonal deviations equation
GMM-type (missing=0, separate instruments for each period unless collapsed)
L(1/15).(L.g_realgdp exprt_arab2009 exprt_nonarab2009) collapsed
Instruments for levels equation
Standard
ln_inv polstab ln_popgrow ln_k exprt_arab2009 exprt_nonarab2009
2003b.years 2004.years 2005.years 2006.years 2007.years 2008.years
2009.years 2010.years 2011.years 2012.years 2013.years 2014.years
2015.years 2016.years 2017.years 2018.years
_cons
GMM-type (missing=0, separate instruments for each period unless collapsed)
D.(L.g_realgdp exprt_arab2009 exprt_nonarab2009) collapsed
------------------------------------------------------------------------------
Arellano-Bond test for AR(1) in first differences: z = -2.65 Pr > z = 0.008
Arellano-Bond test for AR(2) in first differences: z = -0.82 Pr > z = 0.412
------------------------------------------------------------------------------
Sargan test of overid. restrictions: chi2(29) = 30.13 Prob > chi2 = 0.407
(Not robust, but not weakened by many instruments.)
Hansen test of overid. restrictions: chi2(29) = 38.07 Prob > chi2 = 0.121
(Robust, but weakened by many instruments.)



Then I used
xtdpdgmm L(0/1).g_realgdp ln_inv polstab ln_popgrow ln_k exprt_arab2009 exprt_nonarab2009, noserial gmmiv(L.g_realgdp, collapse model(difference)) iv(ln_inv polstab ln_popgrow ln_k exprt_arab2009 exprt_nonarab2009, difference model(difference)) twostep vce(robust)

My result is
Generalized method of moments estimation

Fitting full model:

Step 1:
initial: f(b) = .01873944
alternative: f(b) = 2.7782246
rescale: f(b) = .0030811
Iteration 0: f(b) = .0030811
Iteration 1: f(b) = .00032238
Iteration 2: f(b) = .00032225
Iteration 3: f(b) = .00032225

Step 2:
Iteration 0: f(b) = .48682328
Iteration 1: f(b) = .43199136
Iteration 2: f(b) = .43191809
Iteration 3: f(b) = .43191795

Group variable: id Number of obs = 1361
Time variable: years Number of groups = 106

Moment conditions: linear = 20 Obs per group: min = 2
nonlinear = 12 avg = 12.83962
total = 32 max = 14

(Std. err. adjusted for 106 clusters in id)
-------------------------------------------------------------------------------
| WC-Robust
g_realgdp | Coefficient std. err. z P>|z| [95% conf. interval]
--------------+----------------------------------------------------------------
g_realgdp |
L1. | .4840359 .0788513 6.14 0.000 .3294902 .6385816
|
ln_inv | .0156535 .039496 0.40 0.692 -.0617572 .0930641
polstab | .0080878 .0095458 0.85 0.397 -.0106216 .0267972
ln_popgrow | -.007163 .0039151 -1.83 0.067 -.0148364 .0005104
ln_k | .0530058 .0122549 4.33 0.000 .0289866 .077025
exprt_ar~2009 | .0073165 .0015793 4.63 0.000 .0042211 .0104119
exprt_no~2009 | -.0007791 .0001701 -4.58 0.000 -.0011124 -.0004457
_cons | -1.293861 .2752628 -4.70 0.000 -1.833367 -.7543562
-------------------------------------------------------------------------------
Instruments corresponding to the linear moment conditions:
1, model(diff):
L1.L.g_realgdp L2.L.g_realgdp L3.L.g_realgdp L4.L.g_realgdp L5.L.g_realgdp
L6.L.g_realgdp L7.L.g_realgdp L8.L.g_realgdp L9.L.g_realgdp L10.L.g_realgdp
L11.L.g_realgdp L12.L.g_realgdp L13.L.g_realgdp
2, model(diff):
D.ln_inv D.polstab D.ln_popgrow D.ln_k D.exprt_arab2009 D.exprt_nonarab2009
3, model(level):
_cons

.
end of do-file

. do "/var/folders/gf/3xtyllt9313gwsb7dd1fqw3h0000gp/T//SD00373.000000"

. estat serial

Arellano-Bond test for autocorrelation of the first-differenced residuals
H0: no autocorrelation of order 1 z = -3.5876 Prob > |z| = 0.0003
H0: no autocorrelation of order 2 z = -1.1030 Prob > |z| = 0.2700

. estat overid

Sargan-Hansen test of the overidentifying restrictions
H0: overidentifying restrictions are valid

2-step moment functions, 2-step weighting matrix chi2(24) = 45.7833
Prob > chi2 = 0.0047

2-step moment functions, 3-step weighting matrix chi2(24) = 48.3531
Prob > chi2 = 0.0023

.
My question is: Which result is better??