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This paper examines analytically and experimentally why the system GMM estimator in dynamic panel data models is less biased than the first differencing or the level estimators even though the former uses more instruments. We find that the bias of the system GMM estimator is a weighted sum of...
Persistent link: https://www.econbiz.de/10005489446
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...
Persistent link: https://www.econbiz.de/10005650647
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...
Persistent link: https://www.econbiz.de/10005650686
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...
Persistent link: https://www.econbiz.de/10005675493
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....
Persistent link: https://www.econbiz.de/10005675532
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...
Persistent link: https://www.econbiz.de/10005783963
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...
Persistent link: https://www.econbiz.de/10005675469
In this note, we derive the finite sample bias of the modified ordinary least squares (MOLS) estimator, which was suggested by Wansbeek and Knaap (1999) and reconsidered by Hayakawa (2006a,b). From the formula for the finite sample bias, we find that the bias of the MOLS estimator becomes small...
Persistent link: https://www.econbiz.de/10005416935
In this paper, we show that for panel AR(<italic>p</italic>) models, an instrumental variable (IV) estimator with instruments deviated from past means has the same asymptotic distribution as the infeasible optimal IV estimator when both <italic>N</italic> and <italic>T</italic>, the dimensions of the cross section and time series, are large. If...
Persistent link: https://www.econbiz.de/10004972601
This paper proposes the transformed maximum likelihood estimator for short dynamic panel data models with interactive fixed effects, and provides an extension of Hsiao et al. (2002) that allows for a multifactor error structure. This is an important extension since it retains the advantages of...
Persistent link: https://www.econbiz.de/10010779414