[Arkangel Cordero]

Dear Professor @Sebastian Kripfganz

I have a quick question. At the link below, you mention that:

1. A coefficient of the lagged dependent variable slightly above 1, but not significantly different from 1, can happen in empirical work. It could be a characteristic of the data and does not necessarily imply that there is anything wrong.

https://www.statalist.org/forums/for...08#post1511708

Can you please provide some insight into this in the context of the system-gmm? Could you kindly point to some references where this is indicated? I just want to make sure I am able to preempt reviewer pushback on this matter.

Thank you in advance!

[Sebastian Kripfganz]

A minor update to version 2.6.7 is available for the xtdpdgmm package:

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

While old do-files continue to run, the postestimation command estat serialpm is now no longer documented. It is superseded by the new standalone package xtdpdserial, which can be used as a postestimation command after xtdpdgmm.

[Sebastian Kripfganz]

The Durbin-Wu-Hausman test is not of much use in such dynamic panel models because there are multiple regressors that we can rule out to be strictly exogenous from the very beginning. It is more common in the empirical practice to use incremental overidentification tests (Difference-in-Hansen tests) to check the assumptions behind specific variables.

[Neyati Ahuja]

Dear Prof Sebasian

In my research model i have used system GMM (using xtdpgmm command) since the dependent variable in my study can be influenced by its past values, there exists heteroskedasticity and autocorrelation issue. Further, few of the explanotory variables are also influenced by unobserved factors and have a reverse causality effect on my dependent variable.

I would like to know that besides the post estimation tests of Arellano & Bond and Sargan-Hansen, do we also need to perform endogeneity test (Durbin-Wu-Hausman Test).

Thank You.

[Arkangel Cordero]

Dear Professor @Sebastian Kripfganz

As a follow up, I ran the "weakiv" test after ivreg2 (ssc install weakiv) and obtained the diagnostics below for the same model. Can I conclude that the instruments are strong enough despite the low magnitude of the Weak identification test statistics that come by default in ivreg2?

----------------------------------------
 Test |       Statistic         p-value
------+---------------------------------
  CLR | stat(.)   =   137.16     0.0000
    K | chi2(32)  =    99.89     0.0000
    J | chi2(13)  =    42.29     0.0000
  K-J |        <n.a.>            0.0000
   AR | chi2(44)  =   142.18     0.0000
------+---------------------------------
 Wald | chi2(32)  =   146.58     0.0000
----------------------------------------

[Arkangel Cordero]

Dear Professor @Sebastian Kripfganz


I have a quick question regarding a difference gmm model. The output below comes after successfully reproducing the results for the difference gmm model in xtdpdgmm with xtivreg2 in order to access the instrument diagnostics available for the latter. In general, the diagnostics look fine. The Arellano-Bond autocorrelation test of the residuals look fine as well-- statistically significant ar(1) but statistically insignificant for higher-order autocorrelation in residuals. However, both statistics for the Weak identification test look quite low in magnitude and to complicate things, the "Stock-Yogo weak ID test critical values" are <not available>. My questions are:

1) Is this a matter for concern given the low values of the statistics for the Weak identification test?
2) Is there anything to be done to obtain valid "Stock-Yogo weak ID test critical values"?
3) Do you find these diagnostics concerning?
4) Is there anything to be done at all?

Thank you in advance!

Underidentification test (Kleibergen-Paap rk LM statistic):             98.401
                                                   Chi-sq(14) P-val =   0.0000
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic):                1.345
                         (Kleibergen-Paap rk Wald F statistic):          1.879
Stock-Yogo weak ID test critical values:                       <not available>
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):        15.589
                                                   Chi-sq(12) P-val =   0.1780
-endog- option:
Endogeneity test of endogenous regressors:                              17.543
                                                   Chi-sq(3) P-val =    0.0004

[Ismail Boujnane]

Dear sebastian, I have some cross-sectional (categorical) data collected from a questionnaire in 2021, which are integrated into a longitudinal dataset collected at different points in time for a period of 6 years, from 2015 to 2020. Knowing that the sample is the same for both data collection method, and my categorical data (institutional support, corporate governance) are dynamic, not static, I want to know if integrating them into my panel data is feasible.

[Sebastian Kripfganz]

A new update is available for xtdpdgmm on my personal website. Version 2.6.6 fixes a few bugs in the postestimation command estat serialpm.

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

[Sebastian Kripfganz]

The lags for those instrument that refer to the first-differenced model should be the same for the two estimators; otherwise the results become less easy to compare.

[Sarah Magd]

Dear Prof. Sebastian Kripfganz

Thanks for your constructive replies.

Does the specification of the system GMM have to be the same as the specification of the Diff-GMM? For example, if we use lags(1 3) in the system GMM, do we have to specify the same range of lags in the Diff-GMM? or can the two estimators have different specifications for the range of lags?

[Sebastian Kripfganz]

1. N=28 is still small; therefore, my previous comments still apply.
2. Yes, you can (and probably should) use a diff-GMM estimator as a robustness check (again, preferably one-step only).

[Sarah Magd]

Dear Prof. Sebastian Kripfganz

Thanks for your constructive replies.

1. Are there any issues if we restrict our sample to 28 countries and 13 years? We use a one-step system GMM estimator to estimate our model with this sample. Could you please let us know if we still have any issues with this setup?
2. Given this sample, can we use the diff-GMM for robustness checks? or would you recommend another estimator for robustness?

[Sebastian Kripfganz]

1. I would call this a small N, moderately small T sample. You probably do not need to be concerned much with asymptotic efficiency; it might thus be a good idea to use the one-step insted of the two-step estimator, to avoid estimating the weighting matrix. Also, use the available options (collapsing and lag restrictions) to limit the number of instruments. You could still use the system GMM estimator if you can theoretically justify its assumptions. With such a data set, testing these assumptions empirically is challenging and probably not very reliable.
2. From the outset, we do not know what the true value of the coefficient of the lagged dependent variable is; that is why we are estimating it. There can be different reasons for the observed differences: (i) sampling variability due to the small data set; (ii) endogeneity of the lagged dependent variable (due to neglected serial correlation in the error term) such that the model treating it as predetermined is misspecified; (iii) weak instruments when treating the lagged dependent variable as endogenous, to name a few.

[Sarah Magd]

Dear Prof. Sebastian Kripfganz

1) Can we use the sys-gmm with a sample that has 28 countries and 20 years? Is this considered a big T or can we still use the sys-GMM?
2) When we define the lagged dependent variable as a predetermined variable, the estimated coefficient of this variable is 0.542. However, when we specify the variable as an endogenous, its magnitude becomes .745. Does the magnitude of the lagged dependent variable have to be close to 1?

Could you please guide us on these two points.


Thanks

[Tugrul Cinar]

What you are describing is a data set with repeated cross sections. xtdpdgmm requires the data to be declared as panel data; in particular, a panel identifier variable needs to be declared with xtreg. This may not be possible with the type of data you have.

Thank you very much for the quick response.