Dear all,
I have run a GMM reggresion for GDP/capita. The syntax is xtabond2 y l.y x2 x3 x6 x7 x8 x9 x10 x11 x12 x14 x15 x16 x17 x18 x19 x20 x21 x22 x23 x25 x30 x31 x37 x39 x40 x41 x42 x44 x45 x47 x48 x49 x51 x52 x53 x54 x55 x56 x59 x61 x62 x63 x64 x65 x66, gmmstyle (y x6 x7 x8 x9 x10 x11 x12 x14 x15 x16 x17 x18 x19 x20 x21 x22 x23 x25 x30 x31 x39 x40 x41 x42 x44 x53 x54 x55, lag (2 2)) ivstyle(x2 x3 x37 x45 x47 x48 x49 x51 x52 x56 x59 x61 x62 x63 x64 x65 x66) h(1) nolevel small robust
If I do not separate the vars into exogenous and endogenous like in the following syntax (xtabond2 y l.y x2 x3 x6 x7 x8 x9 x10 x11 x12 x14 x15 x16 x17 x18 x19 x20 x21 x22 x23 x25 x30 x31 x37 x39 x40 x41 x42 x44 x45 x47 x48 x49 x51 x52 x53 x54 x55 x56 x59 x61 x62 x63 x64 x65 x66, gmmstyle (y x2 x3 x6 x7 x8 x9 x10 x11 x12 x14 x15 x16 x17 x18 x19 x20 x21 x22 x23 x25 x30 x31 x37 x39 x40 x41 x42 x44 x45 x47 x48 x49 x51 x52 x53 x54 x55 x56 x59 x61 x62 x63 x64 x65 x66, lag (2 2)) ivstyle(x2 x3 x6 x7 x8 x9 x10 x11 x12 x14 x15 x16 x17 x18 x19 x20 x21 x22 x23 x25 x30 x31 x37 x39 x40 x41 x42 x44 x45 x47 x48 x49 x51 x52 x53 x54 x55 x56 x59 x61 x62 x63 x64 x65 x66) h(1) nolevel small robust), the result are the same.
x61 to x66 are dummy variables (corruption vars) and the rest are dependent vars. I separated the vars to exogenous and endogenous. My questions is if the chi2 = 0.00 Prob > chi2 = 1.000 is considered a problem?
The result are:
Thank you
I have run a GMM reggresion for GDP/capita. The syntax is xtabond2 y l.y x2 x3 x6 x7 x8 x9 x10 x11 x12 x14 x15 x16 x17 x18 x19 x20 x21 x22 x23 x25 x30 x31 x37 x39 x40 x41 x42 x44 x45 x47 x48 x49 x51 x52 x53 x54 x55 x56 x59 x61 x62 x63 x64 x65 x66, gmmstyle (y x6 x7 x8 x9 x10 x11 x12 x14 x15 x16 x17 x18 x19 x20 x21 x22 x23 x25 x30 x31 x39 x40 x41 x42 x44 x53 x54 x55, lag (2 2)) ivstyle(x2 x3 x37 x45 x47 x48 x49 x51 x52 x56 x59 x61 x62 x63 x64 x65 x66) h(1) nolevel small robust
If I do not separate the vars into exogenous and endogenous like in the following syntax (xtabond2 y l.y x2 x3 x6 x7 x8 x9 x10 x11 x12 x14 x15 x16 x17 x18 x19 x20 x21 x22 x23 x25 x30 x31 x37 x39 x40 x41 x42 x44 x45 x47 x48 x49 x51 x52 x53 x54 x55 x56 x59 x61 x62 x63 x64 x65 x66, gmmstyle (y x2 x3 x6 x7 x8 x9 x10 x11 x12 x14 x15 x16 x17 x18 x19 x20 x21 x22 x23 x25 x30 x31 x37 x39 x40 x41 x42 x44 x45 x47 x48 x49 x51 x52 x53 x54 x55 x56 x59 x61 x62 x63 x64 x65 x66, lag (2 2)) ivstyle(x2 x3 x6 x7 x8 x9 x10 x11 x12 x14 x15 x16 x17 x18 x19 x20 x21 x22 x23 x25 x30 x31 x37 x39 x40 x41 x42 x44 x45 x47 x48 x49 x51 x52 x53 x54 x55 x56 x59 x61 x62 x63 x64 x65 x66) h(1) nolevel small robust), the result are the same.
x61 to x66 are dummy variables (corruption vars) and the rest are dependent vars. I separated the vars to exogenous and endogenous. My questions is if the chi2 = 0.00 Prob > chi2 = 1.000 is considered a problem?
The result are:
Code:
xtabond2 y l.y x2 x3 x6 x7 x8 x9 x10 x11 x12 x14 x15 x16 x17 x18 x19 x20 x21 x22 x23 x25 x
> 30 x31 x37 x39 x40 x41 x42 x44 x45 x47 x48 x49 x51 x52 x53 x54 x55 x56 x59, gmmstyle (y x6
> x7 x8 x9 x10 x11 x12 x14 x15 x16 x17 x18 x19 x20 x21 x22 x23 x25 x30 x31 x39 x40 x41 x42
> x44 x53 x54 x55, lag (2 2)) ivstyle(x2 x3 x37 x45 x47 x48 x49 x51 x52 x56 x59) h(1) noleve
> l small robust
Favoring speed over space. To switch, type or click on mata: mata set matafavor space, perm.
Warning: Number of instruments may be large relative to number of observations.
Warning: Two-step estimated covariance matrix of moments is singular.
Using a generalized inverse to calculate robust weighting matrix for Hansen test.
Difference-in-Sargan statistics may be negative.
Dynamic panel-data estimation, one-step difference GMM
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Group variable: Tara Number of obs = 269
Time variable : An Number of groups = 25
Number of instruments = 269 Obs per group: min = 5
F(40, 25) = 1172.68 avg = 10.76
Prob > F = 0.000 max = 18
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| Robust
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
y |
L1. | .2169156 .0872924 2.48 0.020 .0371335 .3966977
|
x2 | 1.763842 .8359274 2.11 0.045 .0422175 3.485467
x3 | 1.341619 1.087861 1.23 0.229 -.8988732 3.582111
x6 | .0853116 .0504325 1.69 0.103 -.018556 .1891792
x7 | .1518393 .0570802 2.66 0.013 .0342805 .2693981
x8 | -.0083792 .0100724 -0.83 0.413 -.0291236 .0123653
x9 | .0736717 .0422614 1.74 0.094 -.0133674 .1607108
x10 | .0211284 .0175226 1.21 0.239 -.01496 .0572169
x11 | .0082324 .0123007 0.67 0.509 -.0171014 .0335662
x12 | -.0430905 .0290536 -1.48 0.151 -.1029275 .0167465
x14 | -.0249491 .0281533 -0.89 0.384 -.0829318 .0330336
x15 | .0610217 .0585641 1.04 0.307 -.0595934 .1816368
x16 | .1525263 .0689312 2.21 0.036 .0105597 .2944928
x17 | -.0088577 .0113725 -0.78 0.443 -.0322797 .0145643
x18 | .0034085 .0253511 0.13 0.894 -.048803 .05562
x19 | -.0917675 .0531611 -1.73 0.097 -.2012548 .0177198
x20 | .0034769 .0516854 0.07 0.947 -.1029713 .1099251
x21 | .0807322 .0558379 1.45 0.161 -.0342681 .1957325
x22 | -.0493855 .0792753 -0.62 0.539 -.212656 .1138851
x23 | -.1820333 .0890977 -2.04 0.052 -.3655335 .0014669
x25 | -.0037304 .0037948 -0.98 0.335 -.0115459 .0040851
x30 | .4657131 .2606787 1.79 0.086 -.0711648 1.002591
x31 | -.1971071 .1993695 -0.99 0.332 -.6077163 .2135021
x37 | .0528869 .0373159 1.42 0.169 -.0239667 .1297405
x39 | .9220377 .1833898 5.03 0.000 .5443394 1.299736
x40 | .0547753 .043517 1.26 0.220 -.0348496 .1444001
x41 | -.0121452 .0356404 -0.34 0.736 -.085548 .0612575
x42 | .0548113 .0336279 1.63 0.116 -.0144467 .1240693
x44 | .0014707 .0019434 0.76 0.456 -.0025318 .0054732
x45 | -.0731522 .1603179 -0.46 0.652 -.4033332 .2570288
x47 | -.1521028 .1869272 -0.81 0.423 -.5370865 .2328808
x48 | -.0401248 .0969941 -0.41 0.683 -.2398879 .1596383
x49 | .0952911 .163293 0.58 0.565 -.241017 .4315993
x51 | .0699631 .1811904 0.39 0.703 -.3032055 .4431317
x52 | .2812747 .1185909 2.37 0.026 .0370321 .5255173
x53 | .1592385 1.178386 0.14 0.894 -2.267692 2.586169
x54 | .3641102 1.222012 0.30 0.768 -2.152671 2.880892
x55 | -.5609728 2.392085 -0.23 0.816 -5.487564 4.365619
x56 | -.0219328 .0208032 -1.05 0.302 -.0647778 .0209122
x59 | .1772366 .1567788 1.13 0.269 -.1456555 .5001286
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Instruments for first differences equation
Standard
D.(x2 x3 x37 x45 x47 x48 x49 x51 x52 x56 x59)
GMM-type (missing=0, separate instruments for each period unless collapsed)
L2.(y x6 x7 x8 x9 x10 x11 x12 x14 x15 x16 x17 x18 x19 x20 x21 x22 x23 x25
x30 x31 x39 x40 x41 x42 x44 x53 x54 x55)
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Arellano-Bond test for AR(1) in first differences: z = -1.62 Pr > z = 0.105
Arellano-Bond test for AR(2) in first differences: z = -0.80 Pr > z = 0.426
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Sargan test of overid. restrictions: chi2(229) = 269.00 Prob > chi2 = 0.036
(Not robust, but not weakened by many instruments.)
Hansen test of overid. restrictions: chi2(229) = 0.00 Prob > chi2 = 1.000
(Robust, but can be weakened by many instruments.)
Difference-in-Hansen tests of exogeneity of instrument subsets:
iv(x2 x3 x37 x45 x47 x48 x49 x51 x52 x56 x59)
Hansen test excluding group: chi2(227) = 0.00 Prob > chi2 = 1.000
Difference (null H = exogenous): chi2(2) = 0.00 Prob > chi2 = 1.000
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