GMM ESTIMATION FOR DYNAMIC PANELS WITH FIXED EFFECTS AND STRONG INSTRUMENTS AT UNITY
针对动态面板数据模型，提出一种避免弱矩条件问题的广义矩估计方法，适用于自回归系数接近1的情况，估计量具有标准高斯渐近性，且小样本偏差小，可用于面板单位根检验。
This paper develops new estimation and inference procedures for dynamic panel data models with fixed effects and incidental trends. A simple consistent GMM estimation method is proposed that avoids the weak moment condition problem that is known to affect conventional GMM estimation when the autoregressive coefficient ( ρ ) is near unity. In both panel and time series cases, the estimator has standard Gaussian asymptotics for all values of ρ ∈ (−1, 1] irrespective of how the composite cross-section and time series sample sizes pass to infinity. Simulations reveal that the estimator has little bias even in very small samples. The approach is applied to panel unit root testing.