Chapter 1: Correction for the Asymptotical Bias of the Arellano-Bond type GMM Estimation of Dynamic Panel Models
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Published:2020
Yonghui Zhang, Qiankun Zhou, 2020. "Correction for the Asymptotical Bias of the Arellano-Bond type GMM Estimation of Dynamic Panel Models", Essays in Honor of Cheng Hsiao, Tong Li, M. Hashem Pesaran, Dek Terrell
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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.
