Efficient GMM Estimation of Dynamic Panel Data Models Where Large Heterogeneity May Be Present
Efficient GMM Estimation of Dynamic Panel Data Models Where Large Heterogeneity May Be Present
复制标题
可能存在较大异质性的动态面板数据模型的有效 GMM 估计
DOI：
10.2139/ssrn.843185
复制
发表时间：
2006
期刊：
影响因子：
--
通讯作者：
Kazuhiko Hayakawa
中科院分区：
文献类型：
--
作者：
Kazuhiko Hayakawa
本文解决了许多工具的问题，即（1）GMM 估计器的偏差和效率之间的权衡，以及（2）动态面板数据模型中的推理不准确，其中不可观察的异质性可能很大。我们发现，如果我们使用级别中的所有工具，尽管 GMM 估计量对大异质性具有鲁棒性，但推断不准确。相反，如果我们在水平中使用最小数量的工具（即每个时期仅使用一种工具），则 GMM 估计器的性能会严重受到异质性程度的影响，即渐近偏差和方差都与异质性的大小成正比。为了解决这个问题，我们提出了一种通过所谓的向后正交偏差变换获得的新形式的工具。渐近分析表明，新工具数量最少的 GMM 估计量比通常使用的估计量（例如水平所有工具的 GMM 估计量、LIML 估计量和组内估计量）具有更小的渐近偏差，而所提出的估计量的渐近方差等于下界。因此，所提出的估计量的渐近偏差和方差同时变小。仿真结果表明，我们的新 GMM 估计器在 RMSE 和推理准确性方面优于所有仪器的传统 GMM 估计器。还提供了西班牙公司数据的实证应用。
This paper addresses the many instruments problem, i.e. (1) the trade-off between the bias and the efficiency of the GMM estimator, and (2) inaccuracy of inference, in dynamic panel data models where unobservable heterogeneity may be large. We find that if we use all the instruments in levels, although the GMM estimator is robust to large heterogeneity, inference is inaccurate. In contrast, if we use the minimum number of instruments in levels in the sense that we use only one instrument for each period, the performance of the GMM estimator is heavily affected by the degree of heterogeneity, that is, both the asymptotic bias and the variance are proportional to the magnitude of heterogeneity. To address this problem, we propose a new form of instruments that are obtained from the so-called backward orthogonal deviation transformation. The asymptotic analysis shows that the GMM estimator with the minimum number of new instruments has smaller asymptotic bias than the estimators typically used such as the GMM estimator with all instruments in levels, the LIML estimators and the within-groups estimators, while the asymptotic variance of the proposed estimator is equal to the lower bound. Thus both the asymptotic bias and the variance of the proposed estimators become small simultaneously. Simulation results show that our new GMM estimator outperforms the conventional GMM estimator with all instruments in levels in term of the RMSE and in terms of accuracy of inference. An empirical application with Spanish firm data is also provided.
影响因子：
2
作者：
Hayakawa, Kazuhiko
通讯作者：
Hayakawa, Kazuhiko