In this paper, we show that the bias-corrected first-difference (BCFD) estimator suggested by Chowdhury (1987) can be applied to the case where the error terms are cross-sectionally dependent and heteroscedastic. By deriving the finite sample bias of the BCFD estimator, we find that the BCFD...

In this paper, we analytically investigate three efficient estimators for cointegrating regression models: Phillips and Hansen's (1990) fully modified OLS estimator, Park's (1992) canonical cointegrating regression estimator, and Saikkonen's (1991) dynamic OLS estimator. First, by the Monte...

In this paper, we show that for panel AR(p) models with iid errors, an instrumental variable (IV) estimator with instruments in the backward orthogonal deviation has the same asymptotic distribution as the infeasible optimal IV estimator when both N and T, the dimensions of the cross section and...

In this paper, we consider the role of "leads" of the first difference of integrated variables in the dynamic OLS estimation of cointegrating regression models. We demonstrate that the role of leads is related to the concept of Granger causality and that in some cases leads are unnecessary in...

This paper complements Alvarez and Arellano (2003) by showing the asymptotic properties of the system GMM estimator for AR(1) panel data models when both N and T tend to infinity. We show that the system GMM estimator with the instruments which Blundell and Bond (1998) used will be inconsistent...

This paper addresses the many instruments problem, i.e. (1) the trade-off between the bias and the efficiency of the GMM estimator, and (2) inaccuracy of inference, in dynamic panel data models where unobservable heterogeneity may be large. We find that if we use all the instruments in levels,...

In this paper, we consider dynamic panel data models with possibly nonstationary initial conditions. We derive the asymptotic properties of the GMM estimators with various kinds of instruments when both N and T are large, where N and T denote the dimensions of the cross section and time series....