The GMM estimator is preferred to the (LSDV) fixed effect estimator because you have panel data and a dynamic model (it includes a lagged dependent variable as a regressor). The GMM dynamic panel estimators are appropriate for large N and small T. For a broader discussion and how to apply GMM methods in Stata see: Roodman, D. (2009). How to do xtabond2: An introduction to "Difference" and "System" GMM in Stata. The Stata Journal, 9(1), 86-136. With very small T unit root testing would not typically be applied. If T is large GMM estimators can become unreliable because the number of instruments becomes large and the instrumented variables can be overfitted and so may not remove the endogenous components of the lagged dependent variable(s) as intended. When N is not large and T is moderate you may wish to use a bias corrected LSDV estimator to deal with dynamic panel bias, although these assume that all variables other than the lagged dependent variable are strictly exogenous. To apply a bias-corrected LSDV estimator to a potentially unbalanced dynamic panel in Stata see: Bruno, G. (2005). Estimation and inference in dynamic unbalanced panel-data models with a small number of individuals. The Sata Journal, 5(4), 473-500. An example paper that uses Bruno's estimator is given in: (1) Goda, T., Stewart, C. and Torres García, A. (2019) ‘Absolute Income Inequality and Rising House Prices’, forthcoming in Socio-Economic Review. An example application of GMM dynamic panel estimators without unit root testing is: Matousek, R., and Nguyen, T. N., and Stewart, C. (2017) ‘Note on a non-structural model using the disequilibrium approach: Evidence from Vietnamese banks’, Research in International Business and Finance, 41, pp. 125 – 135.

Have a look at the following:

Roodman, David. 2009. "A Note on the Theme of Too Many Instruments." Oxford Bulletin of Economics and Statistics, 71(1), 135-58.

Roodman, David 2006. "How to Do Xtabond2: An Introduction to ‘Difference’ and ‘System’ Gmm in Stata," C. f. G. D. W. P. N. 103,

GMM is surely more efficient and not biased as far as the lagged dependent is concerned.

Regards,

Marco

Hello,

should I use system GMM or fixed effects with N=20 and T=10?