Iterated Feasible Generalized Least-Squares Estimation of Augmented Dynamic Panel Data Models
推导了增强型动态面板数据模型迭代可行广义最小二乘估计量的大样本分布，并通过模拟和实际数据比较其与GMM估计量的有限样本性质，发现该估计量表现更优。
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.