Iterated Feasible Generalized Least-Squares Estimation of Augmented Dynamic Panel Data Models

摘要

This article provides the large sample distribution of the iterated feasible generalized least-squares (IFGLS) estimator of an augmented dynamic panel data model. The regressors in the model include lagged values of the dependent variable and may include other explanatory variables that, while exogenous with respect to the time-varying error component, may be correlated with an unobserved time-invariant component. The article compares the finite sample properties of the IFGLS estimator to that of GMM estimators using both simulated and real data and finds that the IFGLS estimator compares favorably.