[Sebastian Kripfganz]

1. It is slightly unusual not to specify the exogenous variables for the first-differenced model as well. I would add:
gmm(TDtoTA SalesGrowth lnTA CFOtoSales CapitalIntensity l.EnvDisclScore, lag(1 3) collapse)

2. The diff option and the model(diff) option do different things. diff first-differences the instruments, while model(diff) specifies that these are instruments for the first-differenced regressors.

3. There is no automatic differencing. You need to specify explicitly what you want the command to do.

[Rupali Vashisht]

Dear Prof. Sebastian Kripfganz ,

I am employing GMM estimation using the xtdpdgmm command. Following is my code:

xtdpdgmm L(0/1).EPS L(0/1).ROAA TDtoTA SalesGrowth lnTA l.EnvDisclScore i.t, model(diff) gmm(L(0/1).EPS L(0/1).ROAA, lag(2 3) collapse) iv(TDtoTA SalesGrowth lnTA CFOtoSales CapitalIntensity l.EnvDisclScore, diff model(level)) iv(i.t) igmm vce(cluster id)

My endogenous variables are L(0/1).EPS and L(0/1).ROAA while the other variables are exogenous. I have the following questions:

1. Do you think my code is correct?
2. I get very different results when I include model(level) suboption in iv() and without it. Could you please help me understand what it means to include 'diff' suboption alongside 'model(level)'suboption? Isn't the first difference transformation automatically done by xtdpdgmm when model (diff) suboption is specified?
3. Finally, are the variables mentioned in gmm() automatically differenced?

Thank you very much for your time.

Kind regards,
Rupali

[Sebastian Kripfganz]

If the dynamic model is the correct model, then any results you obtain for the static model are not reliable because they suffer from an omitted-variable bias. Consequently, the Hausman test for the static model is not reliable. The same is true when some independent variables suffer from endogeneity. You can estimate a dynamic model by GMM in this case, yes, but the Hausman test for the static model has no relevance.

[Sarah Magd]

Thanks Prof. Sebastian Kripfganz ,

Could you please explain your answer more?

I applied the robust standard Hausman test on my static model, and it says that the Random estimator should be applied. However, in the second step, I follow the dynamic specification for my analysis. Also, my independent variables suffer from potential endogeneity due to a reverse causality. Given these two points, can I use the sys-GMM estimator to estimate the dynamic model?

Thanks

[Taka Sakamoto]

Thank you, Sebastian. It's a big help. Thank you for your generous help.

Taka

[Sebastian Kripfganz]

Taka Sakamoto
If you assume that smei4 is an exogenous variables, which is also uncorrelated with any group-specific effects as under a conventional random-effects assumption, then the use of iv(smei4, model(level)) is fine. Lagging the instrument is only necessary if there is concern of contemporaneous correlation (endogeneity) with the idiosyncratic error term. There is no general need to use lag(0 1), but using the extra lagged instrument could potentially provide additional identification strength for some of the other regressors. So, yes, this could be done. Eventually, it all depends on what you assume about the correlations of your variables with the group-specific and the idiosyncratic error component.

In general, the iv() option serves the same purpose as specifying instrumental variables with other estimation commands, such as ivregress.

You might find it easier to use the xtdpdgmmfe command, which is part of the same package but has easier syntax. However, it does not support random-effects type assumptions, as would be required for the above example.

You might also find the following YouTube video useful, where I further explain how to estimate dynamic panel data models with GMM in Stata: https://www.youtube.com/watch?v=5EnP...dvBvwClYxkjp0-

Sarah Magd
I assume you have used the traditional Hausman test for the static model. This requires that all regressors are strictly exogenous, which rules out dynamic models. Under these strong assumptions, no GMM estimator is needed. If you want to account for potential endogeneity, then the traditional Hausman test is not applicable and the results you obtained should not be trusted.

[Taka Sakamoto]

I would be very appreciative if somebody could answer my questions in #600. Thank you for your help in advance.

Best,

Taka

[Sarah Magd]

Dear Prof. Sebastian Kripfganz

The Hausman test shows that the random effect is more accurate to estimate my model. In this case, would this affect anything when specifying my sys-GMM estimator?

Thanks

[Taka Sakamoto]

I have an elementary question about the use of the iv() option in xtdpdgmm. In the command below, "smei4" shows up both as an independent variable and as an instrumental variable in iv(). Is this command problematic? Do I need to lag the instrumental variable like "iv(smei4,lag(1 1) model(level))"? Is it also legitimate to use it like "iv(smei4,lag(0 1) model(level))" or is it problematic again?

Thank you for your help.

. xtdpdgmm gdpgrow smei4 inflation gfcfgrow hfcegrow tradeopen l.lrgdpopc if id~=10,gmm(gdpgrow inflation  gfcf
> grow hfcegrow , lag(2 2) collapse model(diff)) gmm(gdpgrow inflation  gfcfgrow hfcegrow , lag(1 1) diff colla
> pse model(level)) iv(smei4,model(level))  one vce(cl id) small

Generalized method of moments estimation

Fitting full model:
Step 1         f(b) =  .47394918

Group variable: id                           Number of obs         =       541
Time variable: year                          Number of groups      =        20

Moment conditions:     linear =      10      Obs per group:    min =         3
                    nonlinear =       0                        avg =     27.05
                        total =      10                        max =        29

                                    (Std. err. adjusted for 20 clusters in id)
------------------------------------------------------------------------------
             |               Robust
     gdpgrow | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
       smei4 |   .2788973   .0764853     3.65   0.002     .1188117    .4389829
   inflation |   -.026597   .1618153    -0.16   0.871    -.3652804    .3120864
    gfcfgrow |   .2534804   .0579185     4.38   0.000     .1322556    .3747052
    hfcegrow |   .2839644   .2120591     1.34   0.196    -.1598803    .7278091
   tradeopen |  -.0363412    .008684    -4.18   0.001     -.054517   -.0181655
             |
    lrgdpopc |
         L1. |  -.6204065   1.328873    -0.47   0.646     -3.40177    2.160957
             |
       _cons |   9.264398   13.73028     0.67   0.508    -19.47342    38.00221
------------------------------------------------------------------------------
Instruments corresponding to the linear moment conditions:
 1, model(diff):
   L2.gdpgrow L2.inflation L2.gfcfgrow L2.hfcegrow
 2, model(level):
   L1.D.gdpgrow L1.D.inflation L1.D.gfcfgrow L1.D.hfcegrow
 3, model(level):
   smei4
 4, model(level):
   _cons

[Sebastian Kripfganz]

Possible remedies for serial correlation are the inclusion of higher-order autoregressive lags of the dependent variable or distributed lag of the other variables as additional regressors.

[Sarah Magd]

Dear Prof. Sebastian Kripfganz
If the second order autocorrelation is significant, what should I do to control for this rather than adding time fixed effects?

[Sebastian Kripfganz]

No, the bias-corrected estimator implemented in xtdpdbc does not account for reverse causality. It requires all X-regressors to be strictly exogenous in that regard.

[Sarah Magd]

Thanks Prof. Sebastian Kripfganz for your answer in #594

I have one more question. You have introduced a package for Bias Corrected Estimator in Stata. If I am estimating the following model:
Y = L.Y + X1 + X2 + ai + u

If there is a reverse causality from Y to X2, resulting in an endogeneity problem. Would estimating the model with the Bias Corrected Estimator, while controlling for the individual specific effects, control for the endogeneity of X2 as well? or does it only control for that of L.Y?

[Mrisho Rajabu Mrisho]

dear Proffesor @Sebastian Kripfganz,


I am estimating the effects of Islamic banks and convectıonal banks on underground economy within the OIC natıons

I then set up dummıes of Islamic banks to be islamicoic and dummies for non ıslamic banks to be nonislamicoic

code :

[xtabond2 se L.se ATM CBBRNCH10K DEP1KSWTCHCB BORRWERZ1KCB DOMCREDPRVTSECGDP CAPTOASSETRATIO fxreal Taxes gdppercapitagrowthannualnygdppca, robust nomata iv(L2.ATM L2.CBBRNCH10K L2.DEP1KSWTCHCB L2.BORRWERZ1KCB L2.DOMCREDPRVTSECGDP L2.CAPTOASSETRATIO L2.fxreal L2.Taxes L2.gdppercapitagrowthannualnygdppca ) gmm(L.se l.islamicoıc,collapse)]

these are the results ı got are they correct

se	Coef.		St.Err.	t-value		p-value	[95% Conf		Interval]		Sig
L	.968		.033	29.11		0	.903		1.033		***
ATM	0		.001	-0.32		.748	-.002		.001
CBBRNCH10K	0		.001	0.22		.828	-.001		.001
DEP1KSWTCHCB	.001		.001	1.00		.316	-.001		.002
BORRWERZ1KCB	.002		.001	2.63		.008	.001		.004		***
DOMCREDPRVTSECGDP	0		.001	0.84		.402	-.001		.002
CAPTOASSETRATIO	-.003		.001	-2.27		.023	-.006		0		**
fxreal	.003		.001	2.12		.034	0		.006		**
Taxes	-.001		.001	-0.78		.437	-.003		.001
gdppercapitagrowth~a	-.089		.033	-2.67		.008	-.154		-.024		***
Constant	.751		1.271	0.59		.555	-1.74		3.242
Mean dependent var		34.572			SD dependent var			9.808
Number of obs		735			Chi-square			3697.568
*** p<.01, ** p<.05, * p<.1

Instruments for first differences equation

Standard

D.(L2.ATM L2.CBBRNCH10K L2.DEP1KSWTCHCB L2.BORRWERZ1KCB

L2.DOMCREDPRVTSECGDP L2.CAPTOASSETRATIO L2.fxreal L2.Taxes

L2.gdppercapitagrowthannualnygdppca)

GMM-type (missing=0, separate instruments for each period unless collapsed)

L(1/.).(L.se L.islamicoıc) collapsed

Instruments for levels equation

Standard

_cons

L2.ATM L2.CBBRNCH10K L2.DEP1KSWTCHCB L2.BORRWERZ1KCB L2.DOMCREDPRVTSECGDP

L2.CAPTOASSETRATIO L2.fxreal L2.Taxes L2.gdppercapitagrowthannualnygdppca

GMM-type (missing=0, separate instruments for each period unless collapsed)

D.(L.se L.islamicoıc) collapsed

Arellano-Bond test for AR(1) in first differences: z = -3.68 Pr > z = 0.000

Arellano-Bond test for AR(2) in first differences: z = -0.90 Pr > z = 0.366

Sargan test of overid. restrictions: chi2(30) = 40.85 Prob > chi2 = 0.089

(Not robust, but not weakened by many instruments.)

Hansen test of overid. restrictions: chi2(30) = 31.85 Prob > chi2 = 0.375

(Robust, but weakened by many instruments.)

[Sebastian Kripfganz]

1) Such time dummies can typically be treated as fully exogenous.
2) There is no general answer; it depends on whether you can justify the exogeneity of these dummy variables. It certainly seems likely that these dummy variables are correlated with the unobserved country-specific effects. It could be reasonable to assume that they are strictly exogenous with respect to the idiosyncratic error component. With regard to interaction terms, there exogeneity is typically driven by the weakest interaction component; i.e., if the main variable of interest is endogenous, then the interaction terms should normally be treated as endogenous as well.