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Correction for the Asymptotical Bias of the Arellano-Bond type GMM Estimation of Dynamic Panel Models

Yonghui Zhang ^a

Qiankun Zhou ^b

^aSchool of Economics, Renmin University of China, China.

^bDepartment of Economics, Louisiana State University, USA.

Essays in Honor of Cheng Hsiao

ISBN: 978-1-78973-958-9, eISBN: 978-1-78973-957-2

Publication date: 15 April 2020

Abstract

It is shown in the literature that the Arellano–Bond type generalized method of moments (GMM) of dynamic panel models is asymptotically biased (e.g., Hsiao & Zhang, 2015; Hsiao & Zhou, 2017). To correct the asymptotical bias of Arellano–Bond GMM, the authors suggest to use the jackknife instrumental variables estimation (JIVE) and also show that the JIVE of Arellano–Bond GMM is indeed asymptotically unbiased. Monte Carlo studies are conducted to compare the performance of the JIVE as well as Arellano–Bond GMM for linear dynamic panels. The authors demonstrate that the reliability of statistical inference depends critically on whether an estimator is asymptotically unbiased or not.

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Acknowledgements

Acknowledgments

We would like to thank the constructive comments from the editor and an anonymous referee, which has greatly improved our original paper. We also thank the participants of the Advances in Econometrics Conference in honor of Cheng Hsiao for helpful comments. Zhang’s research is sponsored by the National Natural Foundation of China (Grant No. 71401166, 71973141).

Citation

Zhang, Y. and Zhou, Q. (2020), "Correction for the Asymptotical Bias of the Arellano-Bond type GMM Estimation of Dynamic Panel Models", Li, T., Pesaran, M.H. and Terrell, D. (Ed.) Essays in Honor of Cheng Hsiao (Advances in Econometrics, Vol. 41), Emerald Publishing Limited, Leeds, pp. 1-24. https://doi.org/10.1108/S0731-905320200000041001

Publisher

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Emerald Publishing Limited