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It is well-known that generalized method of moments (GMM) estimators ofdynamic panel data models can have asymptotic bias if the number of timeperiods (T) and the number of cross-sectional units (n) are both large (Alvarezand Arellano, 2003). This conclusion, however, follows when all availableinstruments are used. This paper provides results supporting a more optimisticconclusion when less than all available instrumental variables are used. If thenumber of instruments used per period increases with T sufficiently slowly, thebias of GMM estimators based on the forward orthogonal deviationstransformation (FOD-GMM) disappears as T and n increase, regardless of therelative rate of increase in T and n. Monte Carlo evidence is provided thatcorroborates this claim. Moreover, a large-n, large-T distribution result isprovided for FOD-GMM.

Forward Orthogonal Deviations GMM and the Absence of Large Sample Bias