A bias-corrected fixed effects estimator in the dynamic panel data model

Abstract

In this paper, we propose a biased-corrected FE estimator for the dynamic panel data model that works for the autoregressive coefficient $$\rho \in (-1,1]$$ . We further derive the asymptotic result of the suggested bias-corrected FE estimator. We show that when $$\rho =1$$ , the suggested estimator is super-consistent and is more efficient than the existing estimators that also work for $$\rho \in (-1,1]$$ . In addition, when the initial condition is nonstationary, many of the existing dynamic estimators become inconsistent; however, the consistency of the bias-corrected FE estimator we propose does not depend on the stationarity of the initial condition. We also compare the finite sample performances of these estimators using Monte Carlo simulations.