In this paper we show that the Quasi ML estimation method yields consistent Random and Fixed Effects estimators for the autoregression parameter ÃÂ in the panel AR(1) model with arbitrary initial conditions even when the errors are drawn from heterogenous distributions. We compare both analytically and by means of Monte Carlo simulations the QML estimators with the GMM estimator proposed by Arellano and Bond (1991) [AB], which ignores some of the moment conditions implied by the model. Unlike the AB GMM estimator, the QML estimators for ÃÂ only suffer from a weak instruments problem when ÃÂ is close to one if the cross-sectional average of the variances of the errors is constant over time, e.g. under time-series homoskedasticity. However, even in this case the QML estimators are still consistent when ÃÂ is equal to one and they display only a relatively small bias when ÃÂ is close to one. In contrast, the AB GMM estimator is inconsistent when ÃÂ is equal to one, and is severly biased when ÃÂ is close to one. Finally, we study the finite sample properties of two types of estimators for the standard errors of the QML estimators for ÃÂ, and the bounds of QML based confidence intervals for ÃÂ.
Dynamic panel data, Initial conditions, Quasi ML, GMM, Weak moment conditions, Local-to-zero asymptotics