No. 428: GMM Estimation of Dynamic Panel Data Models with Persistent Data
Hugo Kruiniger ,
Queen Mary, University of London
December 1, 2000
This paper considers GMM based estimation and testing procedures for two versions of the AR(1) model with Fixed Effects, henceforth abbreviated as ARFE(1): the conditional ARFE(1) model, and the inclusive ARFE(1) model, which contains the stationary ARFE(1) models and the ARFE(1) model with a unit root. First, the paper presents a two-step Optimal Linear GMM (OLGMM) estimator for the inclusive model which is asymptotically equivalent to the optimal nonlinear GMM estimator of Ahn and Schmidt (1997). Then the paper examines the properties of the GMM estimators for both versions of the model when the data are persistent. Among other things, we find that the OLGMM estimator is superefficient in the unit root case. Furthermore, under stationarity the covariances of the instruments of the Arellano-Bond estimator and the first differences of the dependent variable are not weak. We also derive new approximations to the finite sample distributions of the Arellano-Bond estimator (for both versions of the model), the Arellano-Bover estimator, and the System estimator. We employ local-to-zero asymptotics (cf. Staiger and Stock (1997)) for the Arellano-Bond estimator for the conditional model, because its instruments are weak in this context, and we employ local-to-unity asymptotics, which is developed in this paper, for the estimators for the stationary model. The new approximations agree well with the Monte Carlo evidence in terms of bias and variance. Finally, various GMM based unit root tests against stationary and conditional alternatives are proposed.
J.E.L classification codes: C11, C14, C23
Keywords:Dynamic panel data models, Fixed effects, Generalized Method of Moments, Weak moment conditions, Local-to-zero asymptotics, Local-to-unity asymptotics, Redundancy, Unit root tests, Superefficiency