[Raymond Zhang]

@Sebastian Kripfganz I have recieved your email.Thank you very much.


Best Regards.
Raymond

[Sebastian Kripfganz]

If you are located in China, there might be firewall restrictions that prevent you from installing Stata packages directly from my website. As the new version is not yet available on SSC, I have just sent you the source files for a local installation by e-mail.

[Raymond Zhang]

Dear sebastian Kripfganz,I have tried to update xtdpdgmm from http://www.kripfganz.de/stata/.But I failed to update it.I have sent en email to you.Can you send me the updated adofile and helpfile ?Thanks in advance.



Best.
Raymond

[Sebastian Kripfganz]

There is a new update to version 2.3.2 now available on my personal website:

net install xtdpdgmm, from(http://www.kripfganz.de/stata/) replace

This update fixes an annoying bug that produced an error message when the command was run with the nl(noserial) option on a data set in which the panel identifier was named different than "id". (Apparently, all of my test data sets had a panel identifier variable named "id" which is why I never noticed this bug.) Many thanks to Luca Uberti for flagging this problem.

I used this opportunity to add another option to the estat mmsc postestimation command. The penalty term for the BIC and HQIC versions of the model and moment selection criteria depend on a measure of the sample size, ln(N) and ln(ln(N)), respectively. So far, for N the command always used the number of groups (panels). Now, there is a new option that allows to choose N either as the number of groups, n(groups), the number of clusters, n(cluster), or the total number of observations, n(obs).
If the xtdpdgmm command was run with the vce(cluster) option, the default for estat mmsc is now to use the number of clusters for N. Otherwise, it remains the number of groups. (There is actually a related issue with the conventional BIC after other estimation commands; see help bic note.)

[Nadir Ismayilov]

Dear Sebastian,

I need your help regarding the model. ROA,𝑖𝑡= 𝛼ROA𝑖 + 𝜑ROA𝑖𝑡−1 + ∑ δROA_sic_t SIC_Year sic_t+ εROA,𝑖𝑡;

where:
1)SIC_Year sic_t is the year-specific industry fixed effect.
2)Coefficient 𝛼ROA𝑖 is the firm-specific constant
3)𝜑s are the first-order autoregressive coefficient estimates.

I have used XTABOND command as follows.

. xtabond ROA year2011-year2019 fyear

fyear is time variable in my dataset. It is unbalaced panel data. Gvkey are firm name codes, SIC variable is industry level codes.

In the studies it is written: "We use the deviations of the actual values from the forecasts (i.e., forecast errors) as the measure of abnormal profitability (aROAit) to find firms with greater-than-normal profitability (aROAit>0)"

How would you find forecast and their deviations from actual values? I tried to do it with the forecasting of Stata but as it is unbalanced panel data I receive error each time. With the predict comman I get some big numbers each time, so I am wondering if my Xtabond command was correct and if it was same as in study(not sure if I correctly do it with SIC codes)?

How would you do it with forecast?

Best Regards,
Nadir

[Lin Liu]

That's great. Thank you.

[Sebastian Kripfganz]

Try

test _b[d_L,deputyd:_cons] = _b[o_L,officiald:_cons]

[Lin Liu]

what's wrong with this?
. xtdpdgmm L(0/1).slackz munificence complex dynamics area L(0/1).logasset L(0/1).roa agef edu L(0/1).tenure sex agee L(0/1).deputyd ,
> model(fod) collapse gmm(slackz, lag(1 3)) gmm(munificence, lag(0 1)) gmm(complex, lag(0 1)) gmm(dynamics, lag(0 1)) gmm(area, lag(0 1) ) gmm
> (logasset, lag(1 2)) gmm(roa, lag(1 2)) gmm(agef, lag(0 2)) gmm(edu, lag(0 1)) gmm(sex, lag(0 0) ) gmm(tenure, lag(0 3)) gmm(agee, lag(0 0)
> ) gmm(deputyd, lag(1 2)) gmm(area, 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) diff model(level)) gmm(tenure, lag(0 0) diff model(level)) gmm(sex, lag(0 0) model(level)) gmm(agee, lag(0
> 0) model(level)) gmm(deputyd, lag(1 1) diff model(level)) teffects two vce(r) overid auxiliary

Generalized method of moments estimation

Fitting full model:
Step 1 f(b) = .21961324
Step 2 f(b) = .04905417

Fitting reduced model 1:
Step 1 f(b) = .04378765

Fitting reduced model 2:
Step 1 f(b) = .04737229

Fitting reduced model 3:
Step 1 f(b) = .04174331

Fitting reduced model 4:
Step 1 f(b) = .04725824

Fitting reduced model 5:
Step 1 f(b) = .04894016

Fitting reduced model 6:
Step 1 f(b) = .04896063

Fitting reduced model 7:
Step 1 f(b) = .04693545

Fitting reduced model 8:
Step 1 f(b) = .04642574

Fitting reduced model 9:
Step 1 f(b) = .04471508

Fitting reduced model 10:
Step 1 f(b) = .04683552

Fitting reduced model 11:
Step 1 f(b) = .04554952

Fitting reduced model 12:
Step 1 f(b) = .04759769

Fitting reduced model 13:
Step 1 f(b) = .04775263

Fitting reduced model 15:
Step 1 f(b) = .04901515

Fitting reduced model 16:
Step 1 f(b) = .04815328

Fitting reduced model 17:
Step 1 f(b) = .04883611

Fitting reduced model 18:
Step 1 f(b) = .04345726

Fitting reduced model 19:
Step 1 f(b) = .04868351

Fitting reduced model 20:
Step 1 f(b) = .04871824

Fitting reduced model 21:
Step 1 f(b) = .04833418

Fitting reduced model 22:
Step 1 f(b) = .04895118

Fitting reduced model 23:
Step 1 f(b) = .0457555

Fitting reduced model 24:
Step 1 f(b) = .04576668

Fitting reduced model 25:
Step 1 f(b) = .04623669

Fitting reduced model 26:
Step 1 f(b) = .04865459

Fitting reduced model 27:
Step 1 f(b) = .04898542

Fitting reduced model 28:
Step 1 f(b) = .04787909

Fitting reduced model 29:
Step 1 f(b) = .04526437

Fitting reduced model 30:
Step 1 f(b) = .03214391

Fitting no-mdev model:
Step 1 f(b) = .04428694

Fitting no-level model:
Step 1 f(b) = .0033406

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

Moment conditions: linear = 49 Obs per group: min = 1
nonlinear = 0 avg = 4.44358
total = 49 max = 8

------------------------------------------------------------------------------
slackz | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
/L.slackz | .712158 .1045299 6.81 0.000 .5072831 .9170328
/munificence | -.3096574 .8710623 -0.36 0.722 -2.016908 1.397593
/complex | .0136566 .0495609 0.28 0.783 -.0834811 .1107942
/dynamics | -5.158069 54.91996 -0.09 0.925 -112.7992 102.4831
/area | -.3154719 .1799491 -1.75 0.080 -.6681656 .0372218
/logasset | 7.155498 2.668933 2.68 0.007 1.924485 12.38651
/L.logasset | -8.373998 2.782808 -3.01 0.003 -13.8282 -2.919795
/roa | -16.33088 8.771663 -1.86 0.063 -33.52302 .8612657
/L.roa | 8.227022 5.280975 1.56 0.119 -2.123498 18.57754
/agef | .1133634 .0963832 1.18 0.240 -.0755442 .3022709
/edu | -.501057 .3031709 -1.65 0.098 -1.095261 .0931471
/tenure | .0153419 .0475422 0.32 0.747 -.077839 .1085228
/L.tenure | .0190134 .0677046 0.28 0.779 -.1136852 .1517119
/sex | .7554232 .468428 1.61 0.107 -.1626789 1.673525
/agee | -.0292742 .0188381 -1.55 0.120 -.0661961 .0076477
/deputyd | 1.565541 1.06155 1.47 0.140 -.515058 3.646141
/L.deputyd | -1.890005 1.075679 -1.76 0.079 -3.998297 .2182872
2005.year | .3553894 .2172804 1.64 0.102 -.0704723 .7812512
2006.year | .304873 .2516098 1.21 0.226 -.1882731 .7980191
2007.year | .2514045 .274264 0.92 0.359 -.2861431 .7889521
2008.year | .6769215 .4010137 1.69 0.091 -.109051 1.462894
2009.year | .747258 .3946966 1.89 0.058 -.0263332 1.520849
2010.year | .9056145 .4358076 2.08 0.038 .0514473 1.759782
2011.year | .7428411 .4927214 1.51 0.132 -.2228752 1.708557
/_cons | 12.84346 5.229984 2.46 0.014 2.592877 23.09404
------------------------------------------------------------------------------
Instruments corresponding to the linear moment conditions:
1, model(fodev):
L1.slackz L2.slackz L3.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 L2.agef
9, model(fodev):
edu L1.edu
10, model(fodev):
sex
11, model(fodev):
tenure L1.tenure L2.tenure L3.tenure
12, model(fodev):
agee
13, model(fodev):
L1.deputyd L2.deputyd
15, model(mdev):
sex
16, model(mdev):
agee
17, model(level):
L1.D.slackz
18, model(level):
D.munificence
19, model(level):
D.complex
20, model(level):
D.dynamics
21, model(level):
area
22, model(level):
L1.D.logasset
23, model(level):
L1.D.roa
24, model(level):
D.agef
25, model(level):
D.edu
26, model(level):
D.tenure
27, model(level):
sex
28, model(level):
agee
29, model(level):
L1.D.deputyd
30, model(level):
2005bn.year 2006.year 2007.year 2008.year 2009.year 2010.year 2011.year
31, model(level):
_cons

. est store d

. 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).officiald , model
> (fod) collapse gmm(slackz, lag(1 3)) gmm(munificence, lag(0 2)) gmm(complex, lag(0 2)) gmm(dynamics, lag(0 2)) gmm(area, lag(0 2)) gmm(logas
> set, lag(1 3)) gmm(roa, lag(1 3)) gmm(agef, lag(0 2)) gmm(edu, lag(0 1)) gmm( sex, lag(0 2) ) gmm(tenure, lag(0 2)) gmm(agee, lag(0 2)) gmm
> (officiald, lag(1 3)) gmm(area, 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 mo
> del(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 mo
> del(level)) gmm(edu, lag(0 0) diff model(level)) gmm(tenure, lag(0 0) diff model(level)) gmm(sex, lag(0 0) model(level)) gmm(agee, lag(0 0)
> model(level)) gmm(officiald, lag(1 1) diff model(level)) teffects two vce(r) overid auxiliary

Generalized method of moments estimation

Fitting full model:
Step 1 f(b) = .21879516
Step 2 f(b) = .0895368

Fitting reduced model 1:
Step 1 f(b) = .07797342

Fitting reduced model 2:
Step 1 f(b) = .07882463

Fitting reduced model 3:
Step 1 f(b) = .07606501

Fitting reduced model 4:
Step 1 f(b) = .07926185

Fitting reduced model 5:
Step 1 f(b) = .08893557

Fitting reduced model 6:
Step 1 f(b) = .08534523

Fitting reduced model 7:
Step 1 f(b) = .08495086

Fitting reduced model 8:
Step 1 f(b) = .08775046

Fitting reduced model 9:
Step 1 f(b) = .07315281

Fitting reduced model 10:
Step 1 f(b) = .07129769

Fitting reduced model 11:
Step 1 f(b) = .08573825

Fitting reduced model 12:
Step 1 f(b) = .07303121

Fitting reduced model 13:
Step 1 f(b) = .08466086

Fitting reduced model 15:
Step 1 f(b) = .08889474

Fitting reduced model 16:
Step 1 f(b) = .08735535

Fitting reduced model 17:
Step 1 f(b) = .08696995

Fitting reduced model 18:
Step 1 f(b) = .08947708

Fitting reduced model 19:
Step 1 f(b) = .08952212

Fitting reduced model 20:
Step 1 f(b) = .08911202

Fitting reduced model 21:
Step 1 f(b) = .08279975

Fitting reduced model 22:
Step 1 f(b) = .08536359

Fitting reduced model 23:
Step 1 f(b) = .08768312

Fitting reduced model 24:
Step 1 f(b) = .08707116

Fitting reduced model 25:
Step 1 f(b) = .08927467

Fitting reduced model 26:
Step 1 f(b) = .08936924

Fitting reduced model 27:
Step 1 f(b) = .08820959

Fitting reduced model 28:
Step 1 f(b) = .08916825

Fitting reduced model 29:
Step 1 f(b) = .08449728

Fitting reduced model 30:
Step 1 f(b) = .06366293

Fitting no-mdev model:
Step 1 f(b) = .08117433

Fitting no-level model:
Step 1 f(b) = .04141188

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

Moment conditions: linear = 59 Obs per group: min = 1
nonlinear = 0 avg = 4.44358
total = 59 max = 8

------------------------------------------------------------------------------
slackz | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
/L.slackz | .705772 .1192805 5.92 0.000 .4719866 .9395575
/munificence | -.4403346 .8550542 -0.51 0.607 -2.11621 1.235541
/complex | -.0347037 .0459247 -0.76 0.450 -.1247144 .055307
/dynamics | 59.96618 56.23615 1.07 0.286 -50.25464 170.187
/area | .0132941 .1548448 0.09 0.932 -.2901961 .3167843
/logasset | 4.962371 2.576222 1.93 0.054 -.086932 10.01167
/L.logasset | -5.606148 2.867325 -1.96 0.051 -11.226 .0137069
/roa | -17.11063 5.502293 -3.11 0.002 -27.89493 -6.326336
/L.roa | 10.60277 3.994177 2.65 0.008 2.774331 18.43122
/agef | .0437009 .1054893 0.41 0.679 -.1630544 .2504561
/edu | .0413938 .2862075 0.14 0.885 -.5195625 .6023501
/tenure | .0048285 .0490966 0.10 0.922 -.0913991 .101056
/sex | .3432621 .6145907 0.56 0.576 -.8613135 1.547838
/agee | .0068542 .0191801 0.36 0.721 -.0307382 .0444465
/officiald | 1.023247 .5825883 1.76 0.079 -.1186047 2.165099
/L.officiald | -1.720383 .6982176 -2.46 0.014 -3.088865 -.351902
2005.year | .4571604 .2177951 2.10 0.036 .0302897 .884031
2006.year | .3961391 .2675335 1.48 0.139 -.128217 .9204952
2007.year | .5708944 .254941 2.24 0.025 .0712192 1.07057
2008.year | .7871243 .4278661 1.84 0.066 -.0514777 1.625726
2009.year | .8271088 .4124585 2.01 0.045 .018705 1.635513
2010.year | 1.027484 .4636634 2.22 0.027 .1187209 1.936248
2011.year | .7870365 .5163632 1.52 0.127 -.2250167 1.79909
/_cons | 2.834887 5.29475 0.54 0.592 -7.542632 13.21241
------------------------------------------------------------------------------
Instruments corresponding to the linear moment conditions:
1, model(fodev):
L1.slackz L2.slackz L3.slackz
2, model(fodev):
munificence L1.munificence L2.munificence
3, model(fodev):
complex L1.complex L2.complex
4, model(fodev):
dynamics L1.dynamics L2.dynamics
5, model(fodev):
area L2.area
6, model(fodev):
L1.logasset L2.logasset L3.logasset
7, model(fodev):
L1.roa L2.roa L3.roa
8, model(fodev):
agef L2.agef
9, model(fodev):
edu L1.edu
10, model(fodev):
sex L1.sex L2.sex
11, model(fodev):
tenure L1.tenure L2.tenure
12, model(fodev):
agee L1.agee L2.agee
13, model(fodev):
L1.officiald L2.officiald L3.officiald
15, model(mdev):
sex
16, model(mdev):
agee
17, model(level):
L1.D.slackz
18, model(level):
D.munificence
19, model(level):
D.complex
20, model(level):
D.dynamics
21, model(level):
area
22, model(level):
L1.D.logasset
23, model(level):
L1.D.roa
24, model(level):
D.agef
25, model(level):
D.edu
26, model(level):
D.tenure
27, model(level):
sex
28, model(level):
agee
29, model(level):
L1.D.officiald
30, model(level):
2005bn.year 2006.year 2007.year 2008.year 2009.year 2010.year 2011.year
31, model(level):
_cons

. est store o

. suest d o

Simultaneous results for d, o

Number of obs = 1,142

-------------------------------------------------------------------------------
| Robust
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
--------------+----------------------------------------------------------------
d_L,slackz |
_cons | .712158 .1241785 5.73 0.000 .4687726 .9555434
--------------+----------------------------------------------------------------
d_munificence |
_cons | -.3096574 1.348379 -0.23 0.818 -2.952431 2.333116
--------------+----------------------------------------------------------------
d_complex |
_cons | .0136566 .0717027 0.19 0.849 -.1268782 .1541913
--------------+----------------------------------------------------------------
d_dynamics |
_cons | -5.158069 74.80452 -0.07 0.945 -151.7722 141.4561
--------------+----------------------------------------------------------------
d_area |
_cons | -.3154719 .2452943 -1.29 0.198 -.7962398 .165296
--------------+----------------------------------------------------------------
d_logasset |
_cons | 7.155498 3.376785 2.12 0.034 .5371209 13.77387
--------------+----------------------------------------------------------------
d_L,logasset |
_cons | -8.373998 3.463342 -2.42 0.016 -15.16202 -1.585972
--------------+----------------------------------------------------------------
d_roa |
_cons | -16.33088 9.433864 -1.73 0.083 -34.82091 2.159156
--------------+----------------------------------------------------------------
d_L,roa |
_cons | 8.227022 5.302617 1.55 0.121 -2.165917 18.61996
--------------+----------------------------------------------------------------
d_agef |
_cons | .1133634 .1095392 1.03 0.301 -.1013295 .3280562
--------------+----------------------------------------------------------------
d_edu |
_cons | -.501057 .3479369 -1.44 0.150 -1.183001 .1808867
--------------+----------------------------------------------------------------
d_tenure |
_cons | .0153419 .0756236 0.20 0.839 -.1328777 .1635615
--------------+----------------------------------------------------------------
d_L,tenure |
_cons | .0190134 .0887909 0.21 0.830 -.1550136 .1930403
--------------+----------------------------------------------------------------
d_sex |
_cons | .7554232 .561197 1.35 0.178 -.3445028 1.855349
--------------+----------------------------------------------------------------
d_agee |
_cons | -.0292742 .0263493 -1.11 0.267 -.0809178 .0223694
--------------+----------------------------------------------------------------
d_deputyd |
_cons | 1.565541 2.45065 0.64 0.523 -3.237644 6.368727
--------------+----------------------------------------------------------------
d_L,deputyd |
_cons | -1.890005 2.42004 -0.78 0.435 -6.633196 2.853186
--------------+----------------------------------------------------------------
d_2005,year |
_cons | .3553894 .35439 1.00 0.316 -.3392022 1.049981
--------------+----------------------------------------------------------------
d_2006,year |
_cons | .304873 .3598552 0.85 0.397 -.4004301 1.010176
--------------+----------------------------------------------------------------
d_2007,year |
_cons | .2514045 .4118691 0.61 0.542 -.555844 1.058653
--------------+----------------------------------------------------------------
d_2008,year |
_cons | .6769215 .4867027 1.39 0.164 -.2769983 1.630841
--------------+----------------------------------------------------------------
d_2009,year |
_cons | .747258 .4898263 1.53 0.127 -.212784 1.7073
--------------+----------------------------------------------------------------
d_2010,year |
_cons | .9056145 .5814301 1.56 0.119 -.2339677 2.045197
--------------+----------------------------------------------------------------
d_2011,year |
_cons | .7428411 .5726409 1.30 0.195 -.3795145 1.865197
--------------+----------------------------------------------------------------
d__cons |
_cons | 12.84346 7.006265 1.83 0.067 -.8885694 26.57548
--------------+----------------------------------------------------------------
o_L,slackz |
_cons | .705772 .1116469 6.32 0.000 .4869481 .9245959
--------------+----------------------------------------------------------------
o_munificence |
_cons | -.4403346 1.11817 -0.39 0.694 -2.631908 1.751239
--------------+----------------------------------------------------------------
o_complex |
_cons | -.0347037 .058277 -0.60 0.552 -.1489245 .0795171
--------------+----------------------------------------------------------------
o_dynamics |
_cons | 59.96618 57.7011 1.04 0.299 -53.1259 173.0583
--------------+----------------------------------------------------------------
o_area |
_cons | .0132941 .1780203 0.07 0.940 -.3356192 .3622074
--------------+----------------------------------------------------------------
o_logasset |
_cons | 4.962371 3.533726 1.40 0.160 -1.963606 11.88835
--------------+----------------------------------------------------------------
o_L,logasset |
_cons | -5.606148 3.543803 -1.58 0.114 -12.55187 1.339579
--------------+----------------------------------------------------------------
o_roa |
_cons | -17.11063 6.531034 -2.62 0.009 -29.91122 -4.31004
--------------+----------------------------------------------------------------
o_L,roa |
_cons | 10.60277 4.084201 2.60 0.009 2.597888 18.60766
--------------+----------------------------------------------------------------
o_agef |
_cons | .0437009 .1018201 0.43 0.668 -.1558629 .2432646
--------------+----------------------------------------------------------------
o_edu |
_cons | .0413938 .3063771 0.14 0.893 -.5590943 .6418819
--------------+----------------------------------------------------------------
o_tenure |
_cons | .0048285 .0539864 0.09 0.929 -.100983 .1106399
--------------+----------------------------------------------------------------
o_sex |
_cons | .3432621 .8802884 0.39 0.697 -1.382072 2.068596
--------------+----------------------------------------------------------------
o_agee |
_cons | .0068542 .0200002 0.34 0.732 -.0323456 .0460539
--------------+----------------------------------------------------------------
o_officiald |
_cons | 1.023247 1.430928 0.72 0.475 -1.78132 3.827815
--------------+----------------------------------------------------------------
o_L,officiald |
_cons | -1.720383 1.335567 -1.29 0.198 -4.338046 .8972792
--------------+----------------------------------------------------------------
o_2005,year |
_cons | .4571604 .33954 1.35 0.178 -.2083257 1.122646
--------------+----------------------------------------------------------------
o_2006,year |
_cons | .3961391 .3428259 1.16 0.248 -.2757874 1.068066
--------------+----------------------------------------------------------------
o_2007,year |
_cons | .5708944 .3403995 1.68 0.094 -.0962763 1.238065
--------------+----------------------------------------------------------------
o_2008,year |
_cons | .7871243 .4595304 1.71 0.087 -.1135387 1.687787
--------------+----------------------------------------------------------------
o_2009,year |
_cons | .8271088 .476522 1.74 0.083 -.1068572 1.761075
--------------+----------------------------------------------------------------
o_2010,year |
_cons | 1.027484 .5088123 2.02 0.043 .0302307 2.024738
--------------+----------------------------------------------------------------
o_2011,year |
_cons | .7870365 .5419654 1.45 0.146 -.2751962 1.849269
--------------+----------------------------------------------------------------
o__cons |
_cons | 2.834887 6.433989 0.44 0.659 -9.7755 15.44527
-------------------------------------------------------------------------------

. test [d_L,deputyd=o_L,officiald]
last test not found
r(302);

. test d_L,deputyd=o_L,officiald
invalid 'officiald'
r(198);

. test d_L.deputyd=o_L.officiald
d_L: operator invalid
r(198);

. test [d_L.deputyd=o_L.officiald]
equation d_L not found
r(303);

.

[Sebastian Kripfganz]

Assuming that the coefficients are at a comparable scale, you could probably use the suest command to combine the two regressions. Then you can test for equality of the coefficients with the test command. For this to work, you need to run the two xtdpdgmm regressions with the auxiliary option, store the results with estimates store, and then call suest with the two stored estimation results.

[Lin Liu]

I have two equations of dynamic panel data. One is y=b0+b1x1+b2x2+b3x3, and the other is y=A0+A1x1+A2x4+A3x5. I get the coefficients through the xtdpdgmm. Now, I want to compare b2 and A3 to know which has a larger effect on y. Would you give me some advice?

[Sebastian Kripfganz]

xtdpdgmm does not provide standardized regression coefficients. You would need to standardize all of your variables manually before running the regression.

I can hardly imagine a situation in the context of dynamic panel models where this is meaningful. There is a lot of things that can go wrong and the interpretation of the results becomes anything than straightforward.

[Lin Liu]

Hello Dr. Kripfganz, how can i get standard regression coefficents from your xtdpdgmm? As far as I know，your xtdpdgmm presents nonstandard coefficents.

[Sebastian Kripfganz]

The GMM estimators implemented in xtdpdgmm (or xtabond2) are intended for setting with large N relative to T. Your data set does not really fit into that category. It neither fits into the large-N, large-T world. With small N, you cannot expect to reliably estimate the optimal weighting matrix. Thus, any two-step GMM estimator (that allows for arbitrary correlation within groups over time) is eliminated from the discussion. Similarly, it is not recommended to compute robust standard errors clustered at the group level. You could still use a conventional IV/2SLS estimator with ivregress or xtivreg.

[Piero Best]

Hello Dr. Kripfganz,
I really appreciate all your work.

I am currently having trouble with my dynamic model. N = 15. T = 64.
The empiric evidence in my paper offers a variety of dynamic panels being made and because there is endogeneity I decided to give it a try to your work in order to achieve the best results.

in xtabond2 I had the best model by making all my variables strictly exogenous and only having the lagged depedent variable in the gmm side. But that is not what I intended.

For long panels, which dynamic panel model could I use as an alternative? Or should I just stay with a xtreg fe despite endogeneity being present?

xtabond2 ROA L.ROA creditgrowth Liquidity Provsdefault RatioCapGlobal D.vix Buffer LCR NSFR RR Tier1, gmm(L.ROA. collapse) iv((creditgrowth Liquidity Provsdefault RatioCapGlobal D.vix Buffer LCR NSFR RR Tier1)) robust orthogonal pca

also how can i run this xtabond2 model in xtdpdgmm.

Again i really appreciate all your work, thank you in advance.

[Sebastian Kripfganz]

Maybe the following official video tutorial is of help:
Profile plots and interaction plots in Stata®: Interactions of two continuous variables