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Cited by 301

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Estimation in dynamic panel data models: Improving on the performance of the standard GMM estimator

Blundell, Richard, Bond, Stephen, Windmeijer, Frank·2004·Advances in Econometrics

Abstract

This chapter reviews developments to improve on the poor performance of the standard GMM estimator for highly autoregressive panel series. It considers the use of the ‘system’ GMM estimator that relies on relatively mild restrictions on the initial condition process. This system GMM estimator encompasses the GMM estimator based on the non-linear moment conditions available in the dynamic error components model and has substantial asymptotic efficiency gains. Simulations, that include weakly exogenous covariates, find large finite sample biases and very low precision for the standard first differenced estimator. The use of the system GMM estimator not only greatly improves the precision but also greatly reduces the finite sample bias. An application to panel production function data for the U.S. is provided and confirms these theoretical and experimental findings.

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Metadata

Authors
Blundell, R., Bond, S., & Windmeijer, F.
Venue
Advances in Econometrics
Published
2004-05-19
Pages
53-91
ISSN
0731-9053 (ISSN-L)
OpenAlex
W3124928762
Cited by
301
References
3
FWCI
16.87
Type
book chapter
Language
en
Open access
closed

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CoChat. (2026). Citation check: Estimation in dynamic panel data models: Improving on the performance of the standard GMM estimator. https://app.cochat.ai/papers/10.1016/s0731-9053(00)15003-0
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Blundell, R., Bond, S., & Windmeijer, F. (2004). Estimation in dynamic panel data models: Improving on the performance of the standard GMM estimator. Advances in Econometrics. https://doi.org/10.1016/s0731-9053(00)15003-0

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.