Dear Statalist members,
I have been trying to implement xtdpdsys in my paper. However, based on my data set the Arellano-Bond test is showing second autocorrelation. I tried to add additional lags in order to control for autocorrelation but still it isn't working. As advised before, I am aware that the sample has a very small cross-sectional and a very large time dimension. Perhaps the problem is that I am not treating any of the variables as endogenous. Can you please advise whether this will help? If yes can you please advise how to determine which variables is endogenous?
The table is showing the results with 3 lags on the explanatory variables and 1 lag on the dependent variable (llrgl)
I would really appreciate your help if you have any recommendations or comments.
Best Regards,
Petko Bachvarov
I have been trying to implement xtdpdsys in my paper. However, based on my data set the Arellano-Bond test is showing second autocorrelation. I tried to add additional lags in order to control for autocorrelation but still it isn't working. As advised before, I am aware that the sample has a very small cross-sectional and a very large time dimension. Perhaps the problem is that I am not treating any of the variables as endogenous. Can you please advise whether this will help? If yes can you please advise how to determine which variables is endogenous?
The table is showing the results with 3 lags on the explanatory variables and 1 lag on the dependent variable (llrgl)
Code:
xtdpdsys llrgl L(0/3).(car lerner high_copm ownership_concentration insitution cir deposit_asset netloantotalassets otherearningassets incomediversity size luqidasset gdp_growth inflation) crisis_d gcc_d d_iraq d_bahrain d_syrianarabrepublic d_palestinianterritories d_oman d_tunisia d_yemen d_saudiarabia d_jordan d_kuwait d_iran d_unitedarabemirates d_qatar d_lebanon d_egypt d_morocco d_libya d_algeria d_israel, lags(1) maxldep(1) level(95.0) artests(3) vce(robust)
Code:
System dynamic panel-data estimation Number of obs = 2222
Group variable: y Number of groups = 14
Time variable: banks1
Obs per group: min = 155
avg = 158.7143
max = 159
Number of instruments = 352 Wald chi2(13) = 515.77
Prob > chi2 = 0.0000
One-step results
------------------------------------------------------------------------------
| Robust
llrgl | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
llrgl |
L1. | .0407633 .0193516 2.11 0.035 .0028349 .0786917
|
car |
--. | .0728625 .0182213 4.00 0.000 .0371494 .1085756
L1. | .0630444 .036326 1.74 0.083 -.0081533 .1342421
L2. | -.0337567 .0159734 -2.11 0.035 -.0650639 -.0024494
L3. | .0508269 .0195311 2.60 0.009 .0125466 .0891073
|
lerner |
--. | .1363728 .090361 1.51 0.131 -.0407315 .3134771
L1. | .0351335 .0148506 2.37 0.018 .0060268 .0642402
L2. | .0017673 .0087028 0.20 0.839 -.0152899 .0188245
L3. | -.0502972 .0125289 -4.01 0.000 -.0748534 -.025741
|
high_copm |
--. | -.0483173 .0131467 -3.68 0.000 -.0740844 -.0225503
L1. | .0040067 .0064278 0.62 0.533 -.0085915 .0166049
L2. | -.0043492 .0050352 -0.86 0.388 -.0142181 .0055196
L3. | .0062063 .0074331 0.83 0.404 -.0083623 .0207749
|
ownership~on |
--. | -.0231074 .0061234 -3.77 0.000 -.0351089 -.0111058
L1. | -.0115021 .0040053 -2.87 0.004 -.0193524 -.0036517
L2. | -.0066738 .0047957 -1.39 0.164 -.0160733 .0027257
L3. | -.012933 .0029764 -4.35 0.000 -.0187666 -.0070993
|
insitution |
--. | -.0030289 .0291527 -0.10 0.917 -.0601671 .0541094
L1. | .0032964 .0044128 0.75 0.455 -.0053525 .0119453
L2. | .0043656 .0023974 1.82 0.069 -.0003331 .0090644
L3. | -.0100826 .0038068 -2.65 0.008 -.0175437 -.0026215
|
cir | .0008092 .0003124 2.59 0.010 .0001968 .0014216
deposit_as~t | .017724 .017597 1.01 0.314 -.0167656 .0522135
netloantot~s | .0331774 .0120139 2.76 0.006 .0096307 .0567242
otherearni~s | -.0082478 .0148334 -0.56 0.578 -.0373207 .0208251
incomedive~y | .0085052 .0092001 0.92 0.355 -.0095266 .0265369
size | .0044612 .001072 4.16 0.000 .0023601 .0065623
luqidasset | .0667871 .0285112 2.34 0.019 .0109061 .1226681
gdp_growth | -.4109581 .0946169 -4.34 0.000 -.5964038 -.2255124
inflation | .0026501 .081276 0.03 0.974 -.1566481 .1619482
crisis_d | -.0768883 .0163605 -4.70 0.000 -.1089543 -.0448223
gcc_d | -.0050734 .0604517 -0.08 0.933 -.1235566 .1134098
d_iraq | .0424741 .0491773 0.86 0.388 -.0539116 .1388597
d_bahrain | .0841493 .0366003 2.30 0.021 .0124141 .1558846
d_syrianar~c | .2228825 .0456774 4.88 0.000 .1333565 .3124085
d_palestin~s | -.0174973 .0621769 -0.28 0.778 -.1393619 .1043673
d_oman | -.0048881 .0186836 -0.26 0.794 -.0415073 .0317311
d_tunisia | .030472 .0365098 0.83 0.404 -.041086 .1020299
d_yemen | .3075185 .073833 4.17 0.000 .1628084 .4522286
d_saudiara~a | -.0170867 .0280392 -0.61 0.542 -.0720426 .0378691
d_jordan | .0303965 .0368532 0.82 0.409 -.0418344 .1026274
d_kuwait | .0128424 .0294593 0.44 0.663 -.0448968 .0705817
d_unitedar~s | .0272542 .0159014 1.71 0.087 -.0039119 .0584204
d_lebanon | .0561996 .0302753 1.86 0.063 -.0031388 .115538
d_egypt | .0708349 .033966 2.09 0.037 .0042626 .1374071
d_morocco | .0575885 .0336841 1.71 0.087 -.0084312 .1236081
d_algeria | -.0193776 .0226192 -0.86 0.392 -.0637105 .0249552
d_israel | .0077773 .0635191 0.12 0.903 -.1167179 .1322724
_cons | -.0538288 .053439 -1.01 0.314 -.1585673 .0509096
------------------------------------------------------------------------------
Instruments for differenced equation
GMM-type: L(2/2).llrgl
Standard: D.car LD.car L2D.car L3D.car D.lerner LD.lerner L2D.lerner L3D.lerner D.high_copm LD.high_copm L2D.high_copm L3D.high_copm D.ownership_concentration LD.ownership_concentration
L2D.ownership_concentration L3D.ownership_concentration D.insitution LD.insitution L2D.insitution L3D.insitution D.cir D.deposit_asset D.netloantotalassets D.otherearningassets
D.incomediversity D.size D.luqidasset D.gdp_growth D.inflation D.crisis_d D.gcc_d D.d_iraq D.d_bahrain D.d_syrianarabrepublic D.d_palestinianterritories D.d_oman D.d_tunisia
D.d_yemen D.d_jordan D.d_kuwait D.d_unitedarabemirates D.d_qatar D.d_lebanon D.d_egypt D.d_morocco D.d_algeria D.d_israel
Instruments for level equation
GMM-type: LD.llrgl
Standard: _cons
. estat abond
artests not computed for one-step system estimator with vce(gmm)
Arellano-Bond test for zero autocorrelation in first-differenced errors
+-----------------------+
|Order | z Prob > z|
|------+----------------|
| 1 |-3.1457 0.0017 |
| 2 | 2.1788 0.0293 |
| 3 |-1.5398 0.1236 |
+-----------------------+
H0: no autocorrelation
Best Regards,
Petko Bachvarov
