The Weak Instrument Problem of the System GMM Estimator in Dynamic Panel Data Models

By M. Bun, F. Windmeijer. 2010

545 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/6d0d0cddc0c6ca97e8ce824ea1041ef8e12a2e61

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In Economics and Econometrics

Citations in the corpus

Democracy, Political Risks and Stock Market Performance (2015, unknown): “Moreover, Bun and Windmeijer (2010) have shown that the weak instrument problem may be problematic also for the system GMM approach.”
Too Good to be True: Board Structural Independence as a Moderator of CEO Pay-for-Firm-Performance (2011, unknown): “…variables, it is still based on assumptions pertaining to a static linear model (Wooldridge, 2002), and in the specification of a dynamic panel model, estimates may still be susceptible to contamination from autoregressive processes and the weak instrument problem (Bun and Windmeijer, 2007).”
Estimation of linear dynamic panel data models with time‐invariant regressors (2019, methodology): “This anomaly can be explained by the observation that 21Large size distortions of the Wald test for the system GMM estimator are also documented by Bun and Windmeijer (2010) for the autoregressive parameter.”
Does foreign aid harm political institutions (2015, background): “We recognise that both external and internal instrumental variables procedures to address the endogeneity of aid are controversial (Roodman, 2009; Bun and Windmeijer, 2010; Bazzi and Clemens, 2013).”
Robust Standard Errors in Transformed Likelihood Estimation of Dynamic Panel Data Models (2015, unknown): “But as shown recently by Bun and Windmeijer (2010), the system GMM estimator continues to suﬀer from the weak instruments problem when the variance ratio var ( α i ) /var ( u it ) is large.”
How to Deal With Reverse Causality Using Panel Data? Recommendations for Researchers Based on a Simulation Study (2019, background): “However, despite its wide application in econometrics, the approach is known to suffer from downward bias in face of a large number of moment conditions (Hsiao 2007:90) and weak instruments problems (Bun and Windmeijer 2010), both of which can undermine causal inference.”
THE ASYMPTOTIC PROPERTIES OF THE SYSTEM GMM ESTIMATOR IN DYNAMIC PANEL DATA MODELS WHEN BOTH N AND T ARE LARGE (2015, background): “However, with regard to the first advantage, Bun and Windmeijer (2010) show that the system GMM estimator suffers from the weak instrument problem if the variance ratio of individual effects to the disturbance is large.2 This implies that the weak instrument problem in the GMM estimation of dynamic…”
The Effects of Greenfield FDI and Cross‐border M&As on Total Factor Productivity (2016, unknown): “Because none of the coefficients on the control variables are significantly different from zero in column (2), and also because even the system GMM estimator may suffer from weak instruments (Bun and Windmeijer, 2010), we find the GMM results less reliable than the LSDV results presented in”
Agglomeration and growth: Cross-country evidence☆ (2008, unknown): “Bun and Windmeijer (2007 ) demonstrate that system-GMM consistently has the smallest bias of the dynamic GMM estimators, for the simple reason that it is a weighted average of difference GMM (Arellano–Bond) and levels GMM, and that the biases of those two estimators have opposite signs (with the…”
Redistribution, inequality, and growth: new evidence (2018, background): “Bun and Windmeijer (2010) and Windmeijer (2005) discuss that the finite sample properties of GMM estimators are sensitive to the number of moment conditions used.”