On GMM estimation of linear dynamic panel data models

The linear dynamic panel data model provides a possible avenue to deal with unobservable individual-specific heterogeneity and dynamic relationships in panel data. The model structure renders standard estimation techniques inconsistent. Estimation and inference can, however, be carried out with the generalized method of moments (GMM) by suitably aggregating population orthogonality conditions directly deduced from the underlying modeling assumptions. Different variations of these assumptions are proposed in the literature - often lacking a thorough discussion of the implications for estimation and inference. This paper aims to enhance the understanding of the assumptions and their interplay by connecting the assumptions and the conditions required to establish identification and consistency, derive the asymptotic properties, and carry out inference for the GMM estimator.