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

Yonghui Zhang and Qiankun Zhou

A chapter in Essays in Honor of Cheng Hsiao, 2020, vol. 41, pp 1-24 from Emerald Publishing Ltd

Abstract: 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.

Keywords: Dynamic panel models; generalized method of moments; asymptotical bias; jackknife instrumental variables estimation; statistical inference; bias reduction; C01; C13; C23 (search for similar items in EconPapers)
Date: 2020
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