Hello everyone,
I am new to the list and I hope I am able to keep the rules of good conduct here. I am trying my very best :D

I am running an Panel Vector Autoregressive Model by using the user written command "pvar" based on GMM. I have an unbalanced dataset of 33 banks with yearly data over 7 years
.
Since it is the first time I am running an analysis outside of the OLS universe, I was trying to teach myself some basics about GMM and read some related literature (e.g. "how to xtabond2").
I did my very best in conducting all necessary pre- and post estimation tests and building a sound analysis structure. Nevertheless I still have some issues left, that I can´t solve and I was hoping, that probably some help of you can support my last steps.

What is often emphasized in the gmm literature is the Arellano-Bond autocorrelation test. Unfortunately the pvar command does not provide a post-estimation command for the test, but I have the feeling, that I definately should run this test or at least an equivalent. Do you know a command or a way I can execute the test without the need of duplicating the pvar command by an other gmm command like xtabond? Xtserial dos not seem to be sufficient.
Because I am not sure, if I will be able to do this in an appropriate way.

Also I am wondering, how I can run a test for heteroscedasticity beforehand. Can I build an ARDL model by using the xterg,fe command, then running e.g the. "xttest3" and just using the results of the test when analyzing and building my System GMM model of the pvar command? I am very unsure whether such a mixing of different approaches is a valid proceeding.
At this point probably also the only solution is, to duplicate the pvar estiamtion by an gmm command that supports tests for heteroscedasticity.
The only other option I have in mind is a command that could be used in a similiar "standalone way", like the xtserial command. But I do not know such an command, if there is one available.

I hope (or probably I don´t if this eases the solution finding :D) that my problems are not too simple-minded and that I formulated them understandable.
Thank you very much in advance and stay healthy!