Bias Reduction in Dynamic Panel Data Models by Common Recursive Mean Adjustment*

Abstract

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.