Finite sample evidence of IV estimators under weak instruments
通过模拟和三个实证应用，比较了弱工具变量下不同IV估计量的有限样本表现，发现随机效应拟极大似然估计量在点估计中位数和覆盖率上优于其他方法，但弱识别和中等样本下仍难获得可靠估计。
Abstract We present finite sample evidence on different IV estimators available for linear models under weak instruments; explore the application of the bootstrap as a bias reduction technique to attenuate their finite sample bias; and employ three empirical applications to illustrate and provide insights into the relative performance of the estimators in practice. Our evidence indicates that the random‐effects quasi‐maximum likelihood estimator outperforms alternative estimators in terms of median point estimates and coverage rates, followed by the bootstrap bias‐corrected version of LIML and LIML. However, our results also confirm the difficulty of obtaining reliable point estimates in models with weak identification and moderate‐size samples. Copyright © 2007 John Wiley & Sons, Ltd.