Bias Reduction in Dynamic Panel Data Models by Common Recursive Mean Adjustment*
研究了递归均值调整方法，用于减少动态面板回归中主导根的向下偏误，在截面相关和T较小时表现优于其他估计量。
The within-group estimator (same as the least squares dummy variable estimator) of the dominant root in dynamic panel regression is known to be biased downwards. This article studies recursive mean adjustment (RMA) as a strategy to reduce this bias for AR(p) processes that may exhibit cross-sectional dependence. Asymptotic properties for N,T→∞ jointly are developed. When ( log 2T)(N/T)→ζ, where ζ is a non-zero constant, the estimator exhibits nearly negligible inconsistency. Simulation experiments demonstrate that the RMA estimator performs well in terms of reducing bias, variance and mean square error both when error terms are cross-sectionally independent and when they are not. RMA dominates comparable estimators when T is small and/or when the underlying process is persistent.