I have a panel data, and need to run an IV. I have only 1 endogenous variable.
1) Should I use a Two-stage least squares or a GMM?
2) I understand that GMM is only for dynamic panel data. What is a dynamic panel data?
Thank you
I have a panel data, and need to run an IV. I have only 1 endogenous variable.
1) Should I use a Two-stage least squares or a GMM?
2) I understand that GMM is only for dynamic panel data. What is a dynamic panel data?
Thank you
To answer your first question a number of clarification should be made concerning the terminology
The term dynamic panel data includes all methods, ideas and estimators related to the estimation of dynamic relationships in the framework of a panel data analysis. So "What is dynamic panel data" is not that easy to answer unless you get more specific. Although its hard to tell without any more information I am guessing that in your case you you dont need dynamic panel data estimators since -- so i understand -- you just want to use IV since you have an endogeneity problem and fixed effects would be to costly in terms of efficiency losses.
I think the confusion probably arose because in a panel data context GMM is usually only mentioned in relation to dynamic panel data estimators such as the Arellano Bond or Arellano Bover-type estimators. So to be clear then: despite the fact that the term GMM is mostly used in the dynamic panel data framework it is not something that is intrinsically dynamic.
I hope this helps to clarify some of the misunderstandings.
Kind regards
I think Manuel's answer is overall useful but wrong in stating that it is not easy to answer what dynamic panel data models are. This is relatively simple. Dynamic panel models are models where lags of the outcome variable are included amongst the explanatory variables.
For example:
$$F_{i,t} = aF_{i,t-1} + Bx_{i,t} + u_{i,t}$$
is a dynamic panel model because F appears on the left hand side and a lag of it appears on the right.