Title and Abstract
Generalized method of moments estimation of linear dynamic panel data models
In dynamic models with unobserved group-specific effects, the lagged dependent variable is an endogenous regressor by construction. The conventional fixed-effects estimator is biased and inconsistent under fixed-T asymptotics. To deal with this problem, "difference GMM" and "system GMM" estimators in the spirit of Arellano and Bond (1991, Review of Economic Studies), Arellano and Bover (1995, Journal of Econometrics), and Blundell and Bond (1998, Journal of Econometrics) are predominantly applied in practice. I present the new Stata command xtdpdgmm, that addresses some shortcomings of existing commands and adds further flexibility to the specification of the estimators. In particular, it allows to incorporate the Ahn and Schmidt (1995, Journal of Econometrics) nonlinear moment conditions that can improve the efficiency and robustness of the estimation. Besides the familiar one-step and two-step estimators, xtdpdgmm also provides the Hansen, Heaton, and Yaron (1996, Journal of Business & Economic Statistics) iterated GMM estimator. While it can be pedagogically useful to think about "system GMM" as a system of a level equation and an equation in first differences or forward-orthogonal deviations, I explain that the resulting estimator can still be regarded as a "level GMM" estimator with a set of transformed instruments. These transformed instruments can be obtained in Stata as a postestimation feature and used for subsequent specification tests.
Kripfganz, S. (2019). Generalized method of moments estimation of linear dynamic panel data models.
Proceedings of the 2019 London Stata Conference.