RELIABLE INFERENCE FOR GMM ESTIMATORS? FINITE SAMPLE PROPERTIES OF ALTERNATIVE TEST PROCEDURES IN LINEAR PANEL DATA MODELS

By Stephen R. Bond, F. Windmeijer. 2005

85 citations recorded by the source corpus.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/5bfbb969ab978f591af6a0007451bc7625a6c577

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. R. Poldermans
2. M. Pleus
3. Jan F. Kiviet
4. F. Windmeijer
5. S. Kripfganz
6. M. Bun
7. Kazuhiko Hayakawa
8. M. Falk

Citations in the corpus

• Robust Standard Errors in Transformed Likelihood Estimation of Dynamic Panel Data Models (2015, unknown): “For the two-step GMM estimators, Windmeijer (2005) proposes corrected standard errors for linear static panel data models which are applied to dynamic panel models by Bond and Windmeijer (2005). For the CU-GMM, while it is asymptotically equivalent to the two-step GMM estimator, it is more dispersed”
• Does External Trade Promote Financial Development (2005, unknown): “[16] Bond, S.”
• THE ASYMPTOTIC PROPERTIES OF THE SYSTEM GMM ESTIMATOR IN DYNAMIC PANEL DATA MODELS WHEN BOTH N AND T ARE LARGE (2015, unknown): “See also Bond and Windmeijer (2005).”
• The Weak Instrument Problem of the System GMM Estimator in Dynamic Panel Data Models (2010, unknown): “Furthermore, our theoretical results provide a rationale for the poor performance of the SYS GMM Wald test when data are persistent, as found by Bond and Windmeijer (2005).”
• GMM for Panel Count Data Models (2006, methodology): “Using the notation as in Bond and Windmeijer (2005), the standard two-step Wald test can be computed as a criterion difference Wald = N µ g ³eθb2´0W−1N ³bθ1´ g ³eθb2´− g ³bθ2´0W−1N ³bθ1´ g ³bθ2´¶ , where bθ1 and bθ2 are the one-step and two-step GMM estimators in the unrestricted model, whereas…”
• Accuracy and Efficiency of Various GMM Inference Techniques in Dynamic Micro Panel Data Models (2017, background): “Furthermore, we can report to practitioners: (a) when the effect-noise-ratio is large, the performance of all GMM inference deteriorates; (b) the same occurs in the presence of a genuine (or a supervacaneously treated as) endogenous regressor; (c) in many settings the coefficient restrictions tests ”