Dear all,
I would like to estimate a PP-GARCH model (pooled panel GARCH). Does any of you have an idea how this could be done in Stata?
A collogue told me that some people appear to just declare the data to be a panel (tsset id) and then use the arch command. But what is Stata actually calculating then?
Thanks a lot in advance,
cheers,
Anna

What software and commands can one use to conduct a GARCH panel data analysis?
STATA syntax commands

You can use E views for GARCH (1,1). You open your mean term as an equation and them change the OLS option to ARCH. Then put you GARCH regressors in the GARCH box. You can run your results using the nomal gussian, the student t distribution or the DE distribution.
What software and commands can one use to conduct a GARCH panel data analysis?. Available from: https://www.researchgate.net/post/What_software_and_commands_can_one_use_to_conduct_a_GARCH_panel_data_analysis2 [accessed May 18, 2017].

Your answer Godfrey relates to the simpler part of GARCH applied to observations of 600 upwards of a single entity under investigation that Eviews can easily handle. What I am inquiring about is a panel data analysis (time series and cross-sectional analysis) in GARCH where, say, a single country does not have enough time series observations of up to 600 but when combined with other countries observations are increased to over 600. STATA is likely to have syntax but I am not sure. Anyone who knows?
What software and commands can one use to conduct a GARCH panel data analysis?. Available from: https://www.researchgate.net/post/What_software_and_commands_can_one_use_to_conduct_a_GARCH_panel_data_analysis2 [accessed May 18, 2017].

Stata has several GARCH estimation commands. I am attaching a link to a short youtube video on estimating GARCH models in Stata. I would also suggest looking at the manual entries, since they provide several examples and provide a comprehensive description of the models.
I hope this helps
Ariel
What software and commands can one use to conduct a GARCH panel data analysis?. Available from: https://www.researchgate.net/post/What_software_and_commands_can_one_use_to_conduct_a_GARCH_panel_data_analysis2 [accessed May 18, 2017].

To my best knowledge, the use of GARCH in Panel is quite novel. As a consequence, I do not think that standard GARCH packages cover the estimation of Panel-GARCH. I might suggest you to consider Matlab, and first have a look at Matlab Central, it might be the case that some Matlab users posted there their code.
Which software can be used for ARCH-GHARCH on Panel-Data?. Available from: https://www.researchgate.net/post/Which_software_can_be_used_for_ARCH-GHARCH_on_Panel-Data [accessed May 18, 2017].

Estimating the GARCH(1,1) model on panel data. 05 May 2017, 12:52Hello everyone, I am trying to run a GARCH regression on a panel dataset.
I have an unbalanced panel dataset with gaps, consisting of securities and daily returns. I am trying to find out whether it is possible to run a panel regression of the GARCH(1,1) model and whether this is different to a multivariate GARCH regression. The GARCH model can be found under: Statistics --> Time series --> ARCH/GARCH or Multivariate Time series --> Multivariate GARCH. Both these options have inputs on dependent and independent variables. I am not sure what to fill in as GARCH regresses squared returns on its lags.
Could somebody please shed some light on whether a GARCH panel regression is possible and whether its different to a multivariate GARCH regression. Appreciate any feedback. Thank you.

GMM is just a class of estimator; an estimator that happens to be naturally well suited to deal with potential endogeneity issues. GMM is just an econometric trick in the sense that a hammer is just a tool trick. A hammer doesn't really solve the problem of nails sticking out. Now, carefully applying the hammer to said nails, that is how you solve the problem.
Why do we often use a GMM approach?. Available from: https://www.researchgate.net/post/Why_do_we_often_use_a_GMM_approach [accessed May 18, 2017].

Ok, @Jay that 's is clear.
STATIONARITY OF DATA: in time series, the common model used in autoregressive type and its variations. in autoregressive time series, we are regressing Yt against its own value in the last period Yt-1; thus:
Yt = B0 + B1Xt-1
The value of Yt throughout the period when plotted will not be smooth; there will be some period when there will be spikes (up and down). Let's call this volatility (up and down) the effect of shock. the idea of testing for stationarity is to verify whether the effect of shock is permanent or transitory. if the effect of shock is transient (temporary), the value of Yt in subsequent period will return to its long-run equilibrium. If Yt return to its long-run equilibrium, we say that the data set is stationary, i.e. meaning that the data is stable even with the effect of shock, Yt still goes back to its long-run mean (mean reverting). However, if after the shock, the subsequent Yt does not go back to its long-run equilibrium, its means that the effect of the shock is absorbed into the system and becomes part of the system. This type of data set is called integrated time series. This is one rationale for checking data stationarity.
Is it important to run Stationarity (Unit root) test for Panel data ?. Available from: https://www.researchgate.net/post/Is_it_important_to_run_Stationarity_Unit_root_test_for_Panel_data [accessed May 18, 2017].

AFTER ADF TEST, WHAT 'S NEXT? If the ADF test shows that is stationary at first difference---most cases are--it means that the data is considered stable when lag one period. in general, if the data is stationary after I(1), i.e. after first difference, it means that the data set is stationary. This means that the still retains its memory in mean reverting. In such a case, the data set is predictable. There is no need for error correction mechanism. if the data set lost its mean reverting characteristic, then you would need to implement ECM. What's next? Construct your predictive function (forecast model) and test the model.
REFERENCE: I attach here a reference material that you can use from time to time when doing modeling. I hope it will be helpful.
Is it important to run Stationarity (Unit root) test for Panel data ?. Available from: https://www.researchgate.net/post/Is_it_important_to_run_Stationarity_Unit_root_test_for_Panel_data [accessed May 18, 2017].

Why do we often use a GMM approach? It is often argued that the GMM approach is a second best identification strategy compared to IV approach in case of endogeneity of the explanatory variables. Sometimes, it is also hard to believe that the dependent variable lagged one period can be included as additional explanatory variable. GMM is a more of an econometric trick than a proper solution for endogeneity. Is that argument valid?
Why do we often use a GMM approach?. Available from: https://www.researchgate.net/post/Why_do_we_often_use_a_GMM_approach [accessed May 18, 2017].

I think it would first help to recognize that GMM is a class of estimators that include OLS and 2SLS. That is, there is a way to construct a GMM estimator that is equivalent to the OLS estimator.
Why do we often use a GMM approach?. Available from: https://www.researchgate.net/post/Why_do_we_often_use_a_GMM_approach [accessed May 18, 2017].

With OLS, the number of moment restrictions equals the number of unknown parameters, E[Xe]=0, so this falls into the subset of MM estimators. 2SLS where the number of endogenous variables equals the number of instruments would be another example of an exactly identified GMM estimator using the moment restriction E[Ze]=0, and thus also called MM.
Why do we often use a GMM approach?. Available from: https://www.researchgate.net/post/Why_do_we_often_use_a_GMM_approach [accessed May 18, 2017].

But, you could have more instruments than endogenous variables. Now your system is over-identified with more moment restrictions than parameters to estimate. This is GMM in the fullest sense, though it will lead to the same estimation as typing IVREG in STATA.
Why do we often use a GMM approach?. Available from: https://www.researchgate.net/post/Why_do_we_often_use_a_GMM_approach [accessed May 18, 2017].

Thus, the dichotomy of IV versus GMM is a false one. The AB estimator is both IV and GMM. In AB, the instruments (or in GMM speak--moment restrictions) follow algebraically from assumptions about how the dependent variable is related to the unobservable and time-series properties of the unobservable. The number of instruments is greater than the number of unknown parameters because lagged (and twice-lagged and thrice-lagged) values will tend to be weak instruments, so a large set is often necessary.
Why do we often use a GMM approach?. Available from: https://www.researchgate.net/post/Why_do_we_often_use_a_GMM_approach [accessed May 18, 2017].