[Lin Liu]

I find that your margins and marginsplot can only analyze the interaction of one categorical variable and continuous variable or two categorical variables. But I want to analyze the interaction of two continuous variables, how can I do?

[Sebastian Kripfganz]

I believe you can use margins and marginsplot for that purpose. I have not used them much myself.

[Lin Liu]

Dear,Kripfganz, would you recommend me how to plot the interaction effect of dynamic panel data?

[Sebastian Kripfganz]

I am sorry that the bug had significant effects on your results.

From your results, it seems that there are some issues primarily related to the variables offideputy and offdepdyn. But there is not much more I can say about that. While this is probably not satisfying, sometimes we may just have to live with some imperfection in our models. If you can justify your model specification on theoretical grounds, it might be acceptable to put less emphasis on the specification tests. This also depends on whether the troublesome variables are your main variables of interest or just some control variables. The specification tests then could tell us that we need to be cautious with the interpretation of our results. A perfect model may not exist given the available data.

[Lin Liu]

After you fixed the interaction term bug, the code is the same as the above, but the results are very different.

. xtdpdgmm L(0/1).slackz munificence complex dynamics area L(0/1).logasset L(0/1).roa agef edu tenure sex agee L(0/1).offideputy off
> ideputy#c.dynamics l.offdepdyn, model(fod) collapse gmm(slackz, lag(1 2)) gmm(munificence, lag(0 1)) gmm(complex, lag(0 1)) gmm(dynamics,
> lag(0 1)) gmm(area, lag(0 0)) gmm(logasset, lag(1 2)) gmm(roa, lag(1 2)) gmm(agef, lag(0 1)) gmm(edu, lag(0 0)) gmm(sex, lag(0 0) ) gmm
> (tenure, lag(0 1)) gmm(agee, lag(0 0)) gmm(offideputy, lag(1 2)) gmm(offideputy#c.dynamics, lag(1 2)) gmm(l.offdepdyn, lag(1 2)) gmm(ar
> ea, lag(0 0) model(md)) gmm(edu, lag(0 0) model(md)) gmm( sex, lag(0 0) model(md)) gmm(agee, lag(0 0) model(md)) gmm(slackz, lag(1 1) di
> ff model(level)) gmm(munificence, lag(0 0) diff model(level)) gmm(complex, lag(0 0) diff model(level)) gmm(dynamics, lag(0 0) diff model(
> level)) gmm(area, lag(0 0) model(level)) gmm( logasset, lag(1 1) diff model(level)) gmm( roa, lag(1 1) diff model(level)) gmm( agef, lag(0
> 0) diff model(level)) gmm(edu, lag(0 0) model(level)) gmm(tenure, lag(0 0) diff model(level)) gmm(sex, lag(0 0) model(level)) gmm(ag
> ee , lag(0 0) model(level)) gmm(offideputy, lag(1 1) diff model(level)) gmm(offideputy#c.dynamics, lag(1 1) diff model(level)) gmm(l.offde
> pdyn, lag(1 1) diff model(level)) teffects two vce(r) overid

Generalized method of moments estimation

Fitting full model:
Step 1 f(b) = .1478547
Step 2 f(b) = .08858372

Fitting reduced model 1:
Step 1 f(b) = .07959146

Fitting reduced model 2:
Step 1 f(b) = .08288294

Fitting reduced model 3:
Step 1 f(b) = .06616146

Fitting reduced model 4:
Step 1 f(b) = .07903324

Fitting reduced model 5:
Step 1 f(b) = .08290025

Fitting reduced model 6:
Step 1 f(b) = .08119472

Fitting reduced model 7:
Step 1 f(b) = .08481893

Fitting reduced model 8:
Step 1 f(b) = .08344108

Fitting reduced model 9:
Step 1 f(b) = .08596838

Fitting reduced model 10:
Step 1 f(b) = .08845637

Fitting reduced model 11:
Step 1 f(b) = .08755818

Fitting reduced model 12:
Step 1 f(b) = .08686272

Fitting reduced model 13:
Step 1 f(b) = .0616538

Fitting reduced model 14:
Step 1 f(b) = .05377071

Fitting reduced model 15:
Step 1 f(b) = .07578047

Fitting reduced model 17:
Step 1 f(b) = .08522976

Fitting reduced model 18:
Step 1 f(b) = .08837233

Fitting reduced model 19:
Step 1 f(b) = .08831736

Fitting reduced model 20:
Step 1 f(b) = .08690937

Fitting reduced model 21:
Step 1 f(b) = .08840091

Fitting reduced model 22:
Step 1 f(b) = .08839514

Fitting reduced model 23:
Step 1 f(b) = .08461019

Fitting reduced model 24:
Step 1 f(b) = .07909719

Fitting reduced model 25:
Step 1 f(b) = .08036437

Fitting reduced model 26:
Step 1 f(b) = .08538299

Fitting reduced model 27:
Step 1 f(b) = .08597612

Fitting reduced model 28:
Step 1 f(b) = .08441558

Fitting reduced model 29:
Step 1 f(b) = .08858137

Fitting reduced model 30:
Step 1 f(b) = .08845756

Fitting reduced model 31:
Step 1 f(b) = .08782259

Fitting reduced model 32:
Step 1 f(b) = .08478439

Fitting reduced model 33:
Step 1 f(b) = .08518175

Fitting reduced model 34:
Step 1 f(b) = .08184376

Fitting reduced model 35:
Step 1 f(b) = .03041028

Fitting no-fodev model:
Step 1 f(b) = .00205029

Fitting no-mdev model:
Step 1 f(b) = .06910487

Fitting no-level model:
Step 1 f(b) = .00290704

Group variable: code Number of obs = 1142
Time variable: year Number of groups = 257

Moment conditions: linear = 52 Obs per group: min = 1
nonlinear = 0 avg = 4.44358
total = 52 max = 8

(Std. Err. adjusted for 257 clusters in code)
---------------------------------------------------------------------------------------
| WC-Robust
slackz | Coef. Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
slackz |
L1. | .6368078 .1193084 5.34 0.000 .4029676 .870648
|
munificence | -.662565 .78593 -0.84 0.399 -2.202959 .8778295
complex | -.0397233 .051535 -0.77 0.441 -.1407301 .0612835
dynamics | 42.29624 78.94277 0.54 0.592 -112.4287 197.0212
area | -.0145929 .1966068 -0.07 0.941 -.3999352 .3707494
|
logasset |
--. | 6.389995 3.03789 2.10 0.035 .4358389 12.34415
L1. | -7.461377 3.150692 -2.37 0.018 -13.63662 -1.286134
|
roa |
--. | -10.47766 5.867424 -1.79 0.074 -21.9776 1.022282
L1. | 5.698477 3.432466 1.66 0.097 -1.029033 12.42599
|
agef | .0420754 .0928689 0.45 0.651 -.1399443 .2240951
edu | -.0299814 .0794425 -0.38 0.706 -.1856859 .1257231
tenure | .0034255 .0496194 0.07 0.945 -.0938267 .1006777
sex | .3800276 .4998849 0.76 0.447 -.5997288 1.359784
agee | .0020611 .0118086 0.17 0.861 -.0210832 .0252054
|
offideputy |
--. | 1.167884 3.243949 0.36 0.719 -5.190138 7.525906
L1. | -1.071466 4.479092 -0.24 0.811 -9.850325 7.707393
|
offideputy#c.dynamics |
1 | 15.51207 178.1701 0.09 0.931 -333.6949 364.719
|
offdepdyn |
L1. | -94.22112 231.9872 -0.41 0.685 -548.9076 360.4654
|
year |
2005 | .4139393 .2505291 1.65 0.098 -.0770886 .9049672
2006 | .4621536 .2813397 1.64 0.100 -.0892621 1.013569
2007 | .4226051 .3122018 1.35 0.176 -.1892992 1.034509
2008 | .8698897 .4164206 2.09 0.037 .0537203 1.686059
2009 | .6991356 .3981517 1.76 0.079 -.0812274 1.479499
2010 | 1.047527 .4302469 2.43 0.015 .2042586 1.890795
2011 | .8706683 .4820771 1.81 0.071 -.0741855 1.815522
|
_cons | 7.557025 4.05535 1.86 0.062 -.3913147 15.50536
---------------------------------------------------------------------------------------
Instruments corresponding to the linear moment conditions:
1, model(fodev):
L1.slackz L2.slackz
2, model(fodev):
munificence L1.munificence
3, model(fodev):
complex L1.complex
4, model(fodev):
dynamics
5, model(fodev):
area
6, model(fodev):
L1.logasset L2.logasset
7, model(fodev):
L1.roa L2.roa
8, model(fodev):
agef
9, model(fodev):
edu
10, model(fodev):
sex
11, model(fodev):
tenure L1.tenure
12, model(fodev):
agee
13, model(fodev):
L1.offideputy L2.offideputy
14, model(fodev):
L1.(0b.offideputy#c.dynamics) L2.(0b.offideputy#c.dynamics)
L1.(1.offideputy#c.dynamics) L2.(1.offideputy#c.dynamics)
15, model(fodev):
L2.L.offdepdyn
17, model(mdev):
edu
18, model(mdev):
sex
19, model(mdev):
agee
20, model(level):
L1.D.slackz
21, model(level):
D.munificence
22, model(level):
D.complex
23, model(level):
D.dynamics
24, model(level):
area
25, model(level):
L1.D.logasset
26, model(level):
L1.D.roa
27, model(level):
D.agef
28, model(level):
edu
29, model(level):
D.tenure
30, model(level):
sex
31, model(level):
agee
32, model(level):
L1.D.offideputy
33, model(level):
L1.D.(0b.offideputy#c.dynamics) L1.D.(1.offideputy#c.dynamics)
34, model(level):
L1.D.L.offdepdyn
35, model(level):
2005bn.year 2006.year 2007.year 2008.year 2009.year 2010.year 2011.year
36, model(level):
_cons

. estat serial, ar(1/3)

Arellano-Bond test for autocorrelation of the first-differenced residuals
H0: no autocorrelation of order 1: z = -2.6878 Prob > |z| = 0.0072
H0: no autocorrelation of order 2: z = -0.7629 Prob > |z| = 0.4455
H0: no autocorrelation of order 3: z = 0.7649 Prob > |z| = 0.4444

.
. estat overid

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

2-step moment functions, 2-step weighting matrix chi2(26) = 22.7660
Prob > chi2 = 0.6461

2-step moment functions, 3-step weighting matrix chi2(26) = 25.7361
Prob > chi2 = 0.4777

.
. estat overid, difference

Sargan-Hansen (difference) test of the overidentifying restrictions
H0: (additional) overidentifying restrictions are valid

2-step weighting matrix from full model

| Excluding | Difference
Moment conditions | chi2 df p | chi2 df p
------------------+-----------------------------+-----------------------------
1, model(fodev) | 20.4550 24 0.6706 | 2.3110 2 0.3149
2, model(fodev) | 21.3009 24 0.6209 | 1.4651 2 0.4807
3, model(fodev) | 17.0035 24 0.8485 | 5.7625 2 0.0561
4, model(fodev) | 20.3115 25 0.7303 | 2.4545 1 0.1172
5, model(fodev) | 21.3054 25 0.6755 | 1.4607 1 0.2268
6, model(fodev) | 20.8670 24 0.6466 | 1.8990 2 0.3869
7, model(fodev) | 21.7985 24 0.5913 | 0.9676 2 0.6165
8, model(fodev) | 21.4444 25 0.6676 | 1.3217 1 0.2503
9, model(fodev) | 22.0939 25 0.6303 | 0.6721 1 0.4123
10, model(fodev) | 22.7333 25 0.5931 | 0.0327 1 0.8564
11, model(fodev) | 22.5025 24 0.5493 | 0.2636 2 0.8765
12, model(fodev) | 22.3237 25 0.6170 | 0.4423 1 0.5060
13, model(fodev) | 15.8450 24 0.8936 | 6.9210 2 0.0314
14, model(fodev) | 13.8191 22 0.9078 | 8.9469 4 0.0624
15, model(fodev) | 19.4756 25 0.7738 | 3.2904 1 0.0697
17, model(mdev) | 21.9040 25 0.6413 | 0.8620 1 0.3532
18, model(mdev) | 22.7117 25 0.5944 | 0.0543 1 0.8157
19, model(mdev) | 22.6976 25 0.5952 | 0.0685 1 0.7936
20, model(level) | 22.3357 25 0.6163 | 0.4303 1 0.5118
21, model(level) | 22.7190 25 0.5940 | 0.0470 1 0.8284
22, model(level) | 22.7176 25 0.5941 | 0.0485 1 0.8258
23, model(level) | 21.7448 25 0.6504 | 1.0212 1 0.3122
24, model(level) | 20.3280 25 0.7294 | 2.4380 1 0.1184
25, model(level) | 20.6536 25 0.7118 | 2.1124 1 0.1461
26, model(level) | 21.9434 25 0.6390 | 0.8226 1 0.3644
27, model(level) | 22.0959 25 0.6302 | 0.6702 1 0.4130
28, model(level) | 21.6948 25 0.6533 | 1.0712 1 0.3007
29, model(level) | 22.7654 25 0.5913 | 0.0006 1 0.9804
30, model(level) | 22.7336 25 0.5931 | 0.0324 1 0.8571
31, model(level) | 22.5704 25 0.6026 | 0.1956 1 0.6583
32, model(level) | 21.7896 25 0.6479 | 0.9764 1 0.3231
33, model(level) | 21.8917 24 0.5857 | 0.8743 2 0.6459
34, model(level) | 21.0338 25 0.6907 | 1.7322 1 0.1881
35, model(level) | 7.8154 19 0.9884 | 14.9506 7 0.0366
model(fodev) | 0.5269 1 0.4679 | 22.2391 25 0.6219
model(mdev) | 17.7600 23 0.7704 | 5.0061 3 0.1714
model(level) | 0.7471 3 0.8621 | 22.0189 23 0.5191

.
. underid, overid underid kp sw noreport

collinearity check...
collinearities detected in [Y X] (right to left): 0o.offideputy#co.dynamics
collinearities detected in [Y X Z] (right to left): __alliv_51 __alliv_50 __alliv_49 __alliv_48 __alliv_47 __alliv_46 __alliv_45 __alliv_40
> __alliv_39 __alliv_37 __alliv_33 0o.offideputy#co.dynamics
collinearities detected in [X Z Y] (right to left): 2011.year 2010.year 2009.year 2008.year 2007.year 2006.year 2005bn.year 0o.offideputy#co
> .dynamics agee sex edu area
warning: collinearities detected, reparameterization may be advisable

Overidentification test: Kleibergen-Paap robust LIML-based (LM version)
Test statistic robust to heteroskedasticity and clustering on code
j= 19.93 Chi-sq( 25) p-value=0.7506

Underidentification test: Kleibergen-Paap robust LIML-based (LM version)
Test statistic robust to heteroskedasticity and clustering on code
j= 20.84 Chi-sq( 26) p-value=0.7501

2-step GMM J underidentification stats by regressor:
j= 56.19 Chi-sq( 26) p-value=0.0008 L.slackz
j= 111.04 Chi-sq( 26) p-value=0.0000 munificence
j= 85.75 Chi-sq( 26) p-value=0.0000 complex
j= 23.28 Chi-sq( 26) p-value=0.6700 dynamics
j= 24.35 Chi-sq( 26) p-value=0.6107 area
j= 28.99 Chi-sq( 26) p-value=0.3614 logasset
j= 27.10 Chi-sq( 26) p-value=0.4585 L.logasset
j= 39.83 Chi-sq( 26) p-value=0.0532 roa
j= 36.92 Chi-sq( 26) p-value=0.0965 L.roa
j= 44.47 Chi-sq( 26) p-value=0.0185 agef
j= 41.20 Chi-sq( 26) p-value=0.0394 edu
j= 36.91 Chi-sq( 26) p-value=0.0967 tenure
j= 24.53 Chi-sq( 26) p-value=0.6007 sex
j= 31.68 Chi-sq( 26) p-value=0.2440 agee
j= 11.02 Chi-sq( 26) p-value=0.9972 offideputy
j= 19.34 Chi-sq( 26) p-value=0.8574 L.offideputy
j= 11.02 Chi-sq( 26) p-value=0.9972 0b.offideputy#co.dynamics
j= 11.02 Chi-sq( 26) p-value=0.9972 1.offideputy#c.dynamics
j= 17.37 Chi-sq( 26) p-value=0.9217 L.offdepdyn
j= 74.48 Chi-sq( 26) p-value=0.0000 2005bn.year
j= 74.48 Chi-sq( 26) p-value=0.0000 2006.year
j= 74.48 Chi-sq( 26) p-value=0.0000 2007.year
j= 74.48 Chi-sq( 26) p-value=0.0000 2008.year
j= 74.48 Chi-sq( 26) p-value=0.0000 2009.year
j= 74.48 Chi-sq( 26) p-value=0.0000 2010.year
j= 74.48 Chi-sq( 26) p-value=0.0000 2011.year

[Sebastian Kripfganz]

From a programmer's perspective, factor variables and interaction terms are some of the nastiest animals in the Stata universe. They always find a way to escape your carefully designed algorithms. So it happened again with xtdpdgmm. There was unfortunately an annoying bug that could result in incorrect estimates when interaction terms were specified as instruments. I hopefully now managed to tame these animals once and for all with the latest bug fix.

Version 2.3.1 is now available on my personal website and on SSC (with the usual thanks to Kit Baum).

adoupdate xtdpdgmm, update

[Sebastian Kripfganz]

The underid command with option sw should normally provide you detailed underidentification statistics separately for each regressor. Did you not get those additional statistics? These might tell you for which coefficient there could be an identification problem. Everything else looks good.

Regarding your interaction effect, I believe the categorical variable can take on values 0 or 1, which is why you see separate coefficients. Effectively, you are estimating separate effects for the regressor z when x=0 and when x=1. Your lag specification seems to be alright.

Notice that your example made me aware of a bug in xtdpdgmm that could occur when interaction effects are specified in the list of instruments. I just fixed this bug in a new version that is now available on my personal website. I will make a separate accouncement once it is available on SSC as well.

[Lin Liu]

the code is:
xtdpdgmm L(0/1).slackz munificence complex dynamics area L(0/1).logasset L(0/1).roa agef edu tenure sex agee L(0/1).offideputy offideputy#c.dynamics l.offdepdyn, model(fod) collapse gmm(slackz, lag(1 2)) gmm(munificence, lag(0 1)) gmm(complex, lag(0 1)) gmm(dynamics, lag(0 1)) gmm(area, lag(0 0)) gmm(logasset, lag(1 2)) gmm(roa, lag(1 2)) gmm(agef, lag(0 1)) gmm(edu, lag(0 1)) gmm(sex, lag(0 1) ) gmm (tenure, lag(0 2)) gmm(agee, lag(0 2)) gmm(offideputy, lag(1 2)) gmm(offideputy#c.dynamics, lag(1 2)) gmm(l.offdepdyn, lag(1 2)) gmm(area, lag(0 0) model(md)) gmm(edu, lag(0 0) model(md)) gmm(tenure, lag(0 0) model(md)) gmm( sex, lag(0 0) model(md)) gmm(agee, lag(0 0) model(md)) gmm(slackz, lag(1 1) diff model(level)) gmm(munificence, lag(0 0) diff model(level)) gmm(complex, lag(0 0) diff model(level)) gmm(dynamics, lag(0 0) diff model(level)) gmm(area, lag(0 0) model(level)) gmm( logasset, lag(1 1) diff model(level)) gmm( roa, lag(1 1) diff model(level)) gmm( agef, lag(0 0) diff model(level)) gmm(edu, lag(0 0) model(level)) gmm(tenure, lag(0 0) model(level)) gmm(sex, lag(0 0) model(level)) gmm(agee , lag(0 0) model(level)) gmm(offideputy, lag(1 1) diff model(level)) gmm(offideputy#c.dynamics, lag(1 1) diff model(level)) gmm(l.offdepdyn, lag(1 1) diff model(level)) teffects two vce(r) overid

. xtdpdgmm L(0/1).slackz munificence complex dynamics area L(0/1).logasset L(0/1).roa agef edu tenure sex agee L(0/1).offideputy off
> ideputy#c.dynamics l.offdepdyn, model(fod) collapse gmm(slackz, lag(1 2)) gmm(munificence, lag(0 1)) gmm(complex, lag(0 1)) gmm(dynamics,
> lag(0 1)) gmm(area, lag(0 0)) gmm(logasset, lag(1 2)) gmm(roa, lag(1 2)) gmm(agef, lag(0 1)) gmm(edu, lag(0 0)) gmm(sex, lag(0 0) ) gmm
> (tenure, lag(0 1)) gmm(agee, lag(0 0)) gmm(offideputy, lag(1 2)) gmm(offideputy#c.dynamics, lag(1 2)) gmm(l.offdepdyn, lag(1 2)) gmm(ar
> ea, lag(0 0) model(md)) gmm(edu, lag(0 0) model(md)) gmm( sex, lag(0 0) model(md)) gmm(agee, lag(0 0) model(md)) gmm(slackz, lag(1 1) di
> ff model(level)) gmm(munificence, lag(0 0) diff model(level)) gmm(complex, lag(0 0) diff model(level)) gmm(dynamics, lag(0 0) diff model(
> level)) gmm(area, lag(0 0) model(level)) gmm( logasset, lag(1 1) diff model(level)) gmm( roa, lag(1 1) diff model(level)) gmm( agef, lag(0
> 0) diff model(level)) gmm(edu, lag(0 0) model(level)) gmm(tenure, lag(0 0) diff model(level)) gmm(sex, lag(0 0) model(level)) gmm(ag
> ee , lag(0 0) model(level)) gmm(offideputy, lag(1 1) diff model(level)) gmm(offideputy#c.dynamics, lag(1 1) diff model(level)) gmm(l.offde
> pdyn, lag(1 1) diff model(level)) teffects two vce(r) overid

Generalized method of moments estimation

Fitting full model:
Step 1 f(b) = .14108884
Step 2 f(b) = .07800247

Fitting reduced model 1:
Step 1 f(b) = .06931099

Fitting reduced model 2:
Step 1 f(b) = .07393068

Fitting reduced model 3:
Step 1 f(b) = .06310125

Fitting reduced model 4:
Step 1 f(b) = .07321926

Fitting reduced model 5:
Step 1 f(b) = .07270806

Fitting reduced model 6:
Step 1 f(b) = .06987237

Fitting reduced model 7:
Step 1 f(b) = .07568086

Fitting reduced model 8:
Step 1 f(b) = .07403718

Fitting reduced model 9:
Step 1 f(b) = .07687095

Fitting reduced model 10:
Step 1 f(b) = .07797594

Fitting reduced model 11:
Step 1 f(b) = .0760236

Fitting reduced model 12:
Step 1 f(b) = .07650024

Fitting reduced model 13:
Step 1 f(b) = .06386569

Fitting reduced model 14:
Step 1 f(b) = .05940402

Fitting reduced model 15:
Step 1 f(b) = .05835987

Fitting reduced model 17:
Step 1 f(b) = .07747363

Fitting reduced model 18:
Step 1 f(b) = .07689592

Fitting reduced model 19:
Step 1 f(b) = .07786843

Fitting reduced model 20:
Step 1 f(b) = .07700053

Fitting reduced model 21:
Step 1 f(b) = .07756442

Fitting reduced model 22:
Step 1 f(b) = .07768246

Fitting reduced model 23:
Step 1 f(b) = .07510029

Fitting reduced model 24:
Step 1 f(b) = .07269038

Fitting reduced model 25:
Step 1 f(b) = .07419723

Fitting reduced model 26:
Step 1 f(b) = .07622391

Fitting reduced model 27:
Step 1 f(b) = .07449154

Fitting reduced model 28:
Step 1 f(b) = .07772974

Fitting reduced model 29:
Step 1 f(b) = .0779348

Fitting reduced model 30:
Step 1 f(b) = .07758183

Fitting reduced model 31:
Step 1 f(b) = .0778276

Fitting reduced model 32:
Step 1 f(b) = .07638032

Fitting reduced model 33:
Step 1 f(b) = .0748598

Fitting reduced model 34:
Step 1 f(b) = .06906946

Fitting reduced model 35:
Step 1 f(b) = .03915434

Fitting no-fodev model:
Step 1 f(b) = .00366136

Fitting no-mdev model:
Step 1 f(b) = .06417413

Fitting no-level model:
Step 1 f(b) = .0051203

Group variable: code Number of obs = 1142
Time variable: year Number of groups = 257

Moment conditions: linear = 53 Obs per group: min = 1
nonlinear = 0 avg = 4.44358
total = 53 max = 8

(Std. Err. adjusted for 257 clusters in code)
---------------------------------------------------------------------------------------
| WC-Robust
slackz | Coef. Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
slackz |
L1. | .6218488 .1162067 5.35 0.000 .3940878 .8496098
|
munificence | -.5325147 .7724321 -0.69 0.491 -2.046454 .9814244
complex | -.0403757 .0504071 -0.80 0.423 -.1391718 .0584204
dynamics | 40.72416 82.48774 0.49 0.622 -120.9488 202.3972
area | -.0198439 .2075736 -0.10 0.924 -.4266807 .3869929
|
logasset |
--. | 7.187291 2.761494 2.60 0.009 1.774863 12.59972
L1. | -8.387227 2.801426 -2.99 0.003 -13.87792 -2.896532
|
roa |
--. | -12.11013 6.602533 -1.83 0.067 -25.05085 .8306018
L1. | 6.410398 3.543641 1.81 0.070 -.5350101 13.35581
|
agef | .0583749 .071612 0.82 0.415 -.081982 .1987318
edu | -.0153277 .0769506 -0.20 0.842 -.166148 .1354927
tenure | .0047294 .0374123 0.13 0.899 -.0685974 .0780563
sex | .1996495 .5115984 0.39 0.696 -.8030648 1.202364
agee | .0017578 .0117589 0.15 0.881 -.0212892 .0248048
|
offideputy |
--. | 2.883827 3.854386 0.75 0.454 -4.670631 10.43828
L1. | -2.295162 4.993454 -0.46 0.646 -12.08215 7.491829
|
offideputy#c.dynamics |
1 | -4.082514 195.2103 -0.02 0.983 -386.6876 378.5226
|
offdepdyn |
L1. | -108.3813 231.286 -0.47 0.639 -561.6936 344.931
|
year |
2005 | .3675995 .2461982 1.49 0.135 -.11494 .8501391
2006 | .3671839 .2881837 1.27 0.203 -.1976459 .9320137
2007 | .3655254 .3181113 1.15 0.251 -.2579614 .9890121
2008 | .8464342 .3709715 2.28 0.023 .1193434 1.573525
2009 | .6938291 .3699682 1.88 0.061 -.0312953 1.418953
2010 | .9986676 .3978988 2.51 0.012 .2188003 1.778535
2011 | .8558787 .4232351 2.02 0.043 .0263531 1.685404
|
_cons | 8.629229 3.865083 2.23 0.026 1.053805 16.20465
---------------------------------------------------------------------------------------
Instruments corresponding to the linear moment conditions:
1, model(fodev):
L1.slackz L2.slackz
2, model(fodev):
munificence L1.munificence
3, model(fodev):
complex L1.complex
4, model(fodev):
dynamics L1.dynamics
5, model(fodev):
area
6, model(fodev):
L1.logasset L2.logasset
7, model(fodev):
L1.roa L2.roa
8, model(fodev):
agef
9, model(fodev):
edu
10, model(fodev):
sex
11, model(fodev):
tenure L1.tenure
12, model(fodev):
agee
13, model(fodev):
L1.offideputy L2.offideputy
14, model(fodev):
L2.0b.offideputy#c.dynamics L1.1.offideputy#c.dynamics
L2.1.offideputy#c.dynamics
15, model(fodev):
L1.L.offdepdyn L2.L.offdepdyn
17, model(mdev):
edu
18, model(mdev):
sex
19, model(mdev):
agee
20, model(level):
L1.D.slackz
21, model(level):
D.munificence
22, model(level):
D.complex
23, model(level):
D.dynamics
24, model(level):
area
25, model(level):
L1.D.logasset
26, model(level):
L1.D.roa
27, model(level):
D.agef
28, model(level):
edu
29, model(level):
D.tenure
30, model(level):
sex
31, model(level):
agee
32, model(level):
L1.D.offideputy
33, model(level):
L1.D.0b.offideputy#c.dynamics L1.D.1.offideputy#c.dynamics
34, model(level):
L1.D.L.offdepdyn
35, model(level):
2005bn.year 2006.year 2007.year 2008.year 2009.year 2010.year 2011.year
36, model(level):
_cons

. estat serial, ar(1/3)

Arellano-Bond test for autocorrelation of the first-differenced residuals
H0: no autocorrelation of order 1: z = -2.8390 Prob > |z| = 0.0045
H0: no autocorrelation of order 2: z = -0.5382 Prob > |z| = 0.5905
H0: no autocorrelation of order 3: z = 0.4724 Prob > |z| = 0.6366

.
. estat overid

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

2-step moment functions, 2-step weighting matrix chi2(27) = 20.0466
Prob > chi2 = 0.8288

2-step moment functions, 3-step weighting matrix chi2(27) = 32.1757
Prob > chi2 = 0.2256

.
. estat overid, difference

Sargan-Hansen (difference) test of the overidentifying restrictions
H0: (additional) overidentifying restrictions are valid

2-step weighting matrix from full model

| Excluding | Difference
Moment conditions | chi2 df p | chi2 df p
------------------+-----------------------------+-----------------------------
1, model(fodev) | 17.8129 25 0.8502 | 2.2337 2 0.3273
2, model(fodev) | 19.0002 25 0.7971 | 1.0464 2 0.5926
3, model(fodev) | 16.2170 25 0.9082 | 3.8296 2 0.1474
4, model(fodev) | 18.8173 25 0.8058 | 1.2293 2 0.5408
5, model(fodev) | 18.6860 26 0.8494 | 1.3607 1 0.2434
6, model(fodev) | 17.9572 25 0.8442 | 2.0894 2 0.3518
7, model(fodev) | 19.4500 25 0.7750 | 0.5967 2 0.7421
8, model(fodev) | 19.0276 26 0.8353 | 1.0191 1 0.3127
9, model(fodev) | 19.7558 26 0.8030 | 0.2908 1 0.5897
10, model(fodev) | 20.0398 26 0.7897 | 0.0068 1 0.9342
11, model(fodev) | 19.5381 25 0.7706 | 0.5086 2 0.7755
12, model(fodev) | 19.6606 26 0.8074 | 0.3861 1 0.5344
13, model(fodev) | 16.4135 25 0.9020 | 3.6332 2 0.1626
14, model(fodev) | 15.2668 24 0.9127 | 4.7798 3 0.1886
15, model(fodev) | 14.9985 25 0.9414 | 5.0481 2 0.0801
17, model(mdev) | 19.9107 26 0.7958 | 0.1359 1 0.7124
18, model(mdev) | 19.7623 26 0.8027 | 0.2844 1 0.5938
19, model(mdev) | 20.0122 26 0.7910 | 0.0344 1 0.8528
20, model(level) | 19.7891 26 0.8014 | 0.2575 1 0.6118
21, model(level) | 19.9341 26 0.7947 | 0.1126 1 0.7372
22, model(level) | 19.9644 26 0.7932 | 0.0822 1 0.7743
23, model(level) | 19.3008 26 0.8235 | 0.7459 1 0.3878
24, model(level) | 18.6814 26 0.8496 | 1.3652 1 0.2426
25, model(level) | 19.0687 26 0.8335 | 0.9779 1 0.3227
26, model(level) | 19.5895 26 0.8106 | 0.4571 1 0.4990
27, model(level) | 19.1443 26 0.8303 | 0.9023 1 0.3422
28, model(level) | 19.9765 26 0.7927 | 0.0701 1 0.7912
29, model(level) | 20.0292 26 0.7902 | 0.0174 1 0.8951
30, model(level) | 19.9385 26 0.7945 | 0.1081 1 0.7423
31, model(level) | 20.0017 26 0.7915 | 0.0449 1 0.8321
32, model(level) | 19.6297 26 0.8088 | 0.4169 1 0.5185
33, model(level) | 19.2390 25 0.7855 | 0.8077 2 0.6678
34, model(level) | 17.7509 26 0.8846 | 2.2958 1 0.1297
35, model(level) | 10.0627 20 0.9670 | 9.9840 7 0.1895
model(fodev) | 0.9410 1 0.3320 | 19.1057 26 0.8319
model(mdev) | 16.4928 24 0.8695 | 3.5539 3 0.3138
model(level) | 1.3159 4 0.8587 | 18.7307 23 0.7167

.
. underid, overid underid kp sw noreport

collinearity check...
collinearities detected in [Y X] (right to left): 0o.offideputy#co.dynamics
collinearities detected in [Y X Z] (right to left): __alliv_52 __alliv_51 __alliv_50 __alliv_49 __alliv_48 __alliv_47 __alliv_46 __alliv_41
> __alliv_40 __alliv_38 __alliv_34 0o.offideputy#co.dynamics
collinearities detected in [X Z Y] (right to left): 2011.year 2010.year 2009.year 2008.year 2007.year 2006.year 2005bn.year 0o.offideputy#co
> .dynamics agee sex edu area
warning: collinearities detected, reparameterization may be advisable

Overidentification test: Kleibergen-Paap robust LIML-based (LM version)
Test statistic robust to heteroskedasticity and clustering on code
j= 16.32 Chi-sq( 26) p-value=0.9283

Underidentification test: Kleibergen-Paap robust LIML-based (LM version)
Test statistic robust to heteroskedasticity and clustering on code
j= 34.95 Chi-sq( 27) p-value=0.1400

what's wrong with it? Another question: when the lag of one categorical variable times the lag of another continuous varible, the code is l.x#cl.z(x is categorial, z is continuous), the outcome has two lines of results, line 0 and line 1. What's the meaning? How do I specify the lag of one categorical variable times the lag of another continuous variable?

[Sebastian Kripfganz]

To provide further help, I would need to see your command lines and Stata output (with CODE delimiters) following the advice in the Statalist FAQs?

[Lin Liu]

Thanks. I have some other doubts. Among the serial correlation test, overidentification test, incremental overidentification test, and underidentification test, the former three tests are very easy to pass, however, the underidentification test is very difficult to （even always not）pass,why? What's more，changing the order of the lag of the instruments will lead to largely vary in the underidentification test, but not vary in the former three tests,why?

[Sebastian Kripfganz]

Hello,why the serial correlation test and the overidentificaton test are easy to pass, while the underidentification test is difficult to pass? What are main possible resons?

Notice that for the underidentification test the null hypothesis is that the model is indeed underidentified. Therefore, you actually want to reject the null hypothesis.
For the overidentification test, the null hypothesis is that the model is correctly specified. Therefore, here you do not want to reject the null hypothesis.

If the model is underidentified, then the overidentification tests may not be very reliable because they rely on the maintained assumption that there are at least as many valid instruments available as is needed to identify all coefficients.

[Lin Liu]

Hello,why the serial correlation test and the overidentificaton test are easy to pass, while the underidentification test is difficult to pass? What are main possible resons?

[Tugrul Cinar]

Thank you for your quick reply. I havent think of using an interaction term. That was an enlightening advice.

[Sebastian Kripfganz]

It is correct that xtdpdgmm does not do anything to specifically address cross-sectional dependence. Time dummies can account for cross-sectional dependence due to common shocks assuming homogeneity of the effects of these shocks across units. Any other variables that are constant across units but vary over time become redundant in the presence of time dummies, unless you create interaction terms of these common-shock variables with variables that vary across units. The latter could be away to approximate heterogenous effects of common shocks conditional on observed variables. Obviously, all of this is more restrictive than other approaches for large-N, large-T panel models with common factors / interactive fixed effects, but xtdpdgmm is primarily intended for small-T data.

[Tugrul Cinar]

Dear Sebastian,

I am using xtdpdgmm for my research. As far as i know xtdpdgmm (and gmm estimation in general) does not account for cross sectional dependency.

My data (maybe most of the panel data) suffers from cross sectional dependency and i tried to use extra variables to capture time varying common factors across cross sections to eliminate this problem. But later i realised that we are already using time dummies as regressors with the teffects option (or manually). Since (strong) cross-sectional dependence arise from time varying common shocks, aren't we eliminating it by adding year dummies as regressors? Will any other extra variables to capture time varying common shocks other than time dummies be redundant in this occasion?

Thanks in advance.