工具变量估计中的非渐近推理

J. Horowitz
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引用次数: 5

摘要

本文提出了一种在弱假设条件下对各种可能的非线性IV模型进行推理的简单方法。该方法是非渐近的,因为它提供了拒绝正确零假设的真实概率和名义概率之差的有限样本界。该方法是安德森-鲁宾测试的非学生化版本,但动机和分析方式不同。与传统的Anderson-Rubin检验相比,本文提出的方法不需要限制性的分布假设、估计模型的线性性或联立方程。它也不需要知道这些工具是强是弱。它不需要测试或估计仪器的强度。该方法可应用于可能是非线性的分位数IV模型,并可用于对非参数替代的参数IV模型进行测试。无论仪器的强度如何,这里给出的结果都适用于有限的样品。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Non-asymptotic inference in instrumental variables estimation
This paper presents a simple method for carrying out inference in a wide variety of possibly nonlinear IV models under weak assumptions. The method is non-asymptotic in the sense that it provides a finite sample bound on the difference between the true and nominal probabilities of rejecting a correct null hypothesis. The method is a non-Studentized version of the Anderson-Rubin test but is motivated and analyzed differently. In contrast to the conventional Anderson-Rubin test, the method proposed here does not require restrictive distributional assumptions, linearity of the estimated model, or simultaneous equations. Nor does it require knowledge of whether the instruments are strong or weak. It does not require testing or estimating the strength of the instruments. The method can be applied to quantile IV models that may be nonlinear and can be used to test a parametric IV model against a nonparametric alternative. The results presented here hold in finite samples, regardless of the strength of the instruments.
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