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英文文献：Optimal Inference for Instrumental Variables Regression with non-Gaussian Errors-非高斯误差下工具变量回归的最优推理
英文文献作者：Mathias D. Cattaneo,Richard K. Crump,Michael Jansson
英文文献摘要：
This paper is concerned with inference on the coefficient on the endogenous regressor in a linear instrumental variables model with a single endogenous regressor, nonrandom exogenous regressors and instruments, and i.i.d. errors whose distribution is unknown. It is shown that under mild smoothness conditions on the error distribution it is possible to develop tests which are “nearly” efficient when identification is weak and consistent and asymptotically optimal when identification is strong. In addition, an estimator is presented which can be used in the usual way to construct valid (indeed, optimal) confidence intervals when identification is strong. The estimator is of the two stage least squares variety and is asymptotically efficient under strong identification whether or not the errors are normal.
摘要研究了在单一内源性回归变量、非随机外源性回归变量和工具以及分布未知的i.i.d.误差的线性工具变量模型中，内源性回归变量的系数的推断。结果表明，在误差分布的温和平滑条件下，当辨识度较弱时，可以开发出“接近”有效的判别方法;当辨识度较强时，可以开发出渐近最优的判别方法。此外，还提出了一种估计器，当辨识度较强时，它可以用通常的方法来构造有效的(实际上是最优的)置信区间。该估计量是二阶最小二乘簇，无论误差是否正态，在强识别下都是渐近有效的。
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