X-DIFFERENCING AND DYNAMIC PANEL MODEL ESTIMATION
提出一种新的动态面板模型估计方法X差分，通过系统差分消除固定效应，在单位根附近仍保持信息强度，得到面板完全聚合估计量，适用于大截面或长时间序列，且优于偏差校正最小二乘和GMM等方法。
This paper introduces a new estimation method for dynamic panel models with fixed effects and AR( p ) idiosyncratic errors. The proposed estimator uses a novel form of systematic differencing, called X-differencing, that eliminates fixed effects and retains information and signal strength in cases where there is a root at or near unity. The resulting “panel fully aggregated” estimator (PFAE) is obtained by pooled least squares on the system of X-differenced equations. The method is simple to implement, consistent for all parameter values, including unit root cases, and has strong asymptotic and finite sample performance characteristics that dominate other procedures, such as bias corrected least squares, generalized method of moments (GMM), and system GMM methods. The asymptotic theory holds as long as the cross section ( n ) or time series ( T ) sample size is large, regardless of the n / T ratio, which makes the approach appealing for practical work. In the time series AR(1) case ( n = 1), the FAE estimator has a limit distribution with smaller bias and variance than the maximum likelihood estimator (MLE) when the autoregressive coefficient is at or near unity and the same limit distribution as the MLE in the stationary case, so the advantages of the approach continue to hold for fixed and even small n . Some simulation results are reported, giving comparisons with other dynamic panel estimation methods.