w_it is a vector of predetermined covariates (which may include the lag of y) and endogenous covariates, all of which may be correlated with the v_i (Predetermined variables are potentially correlated with past errors. Endogenous ones are potentially correlated with past and present errors.);
Moreover, though it is designed for dynamic models, xtabond2 does not require the lagged dependent variable to appear on the right hand side.
Roodman(2006)
可以用于静态面板,解决内生性问题。
除了前面有提到的Roodman在How to do xtabond2里面提到的右侧可以不加因变量滞后项那个证明,我在ISR上找到一篇文献:2018 - ISR - The Impact of User Personality Traits on Word of Mouth Text-Mining Social Media Platforms,也是用GMM处理静态面板,然后汇报AR(1)以及AR(2)的。计量上的估计方法大致是OLS\MLE\MM,而不同的估计方法有不同的假定,我觉得本质上不在于数据是动态还是静态,而是假定是否适用。
以供讨论学习。