Weak Instruments in Instrumental Variables Regression: Theory and Practice
Weak Instruments in Instrumental Variables Regression: Theory and Practice
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DOI：
10.1146/annurev-economics-080218-025643
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发表时间：
2019-01-01
期刊：
影响因子：
--
通讯作者：
Sun, Liyang
中科院分区：
文献类型：
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作者：
Andrews, Isaiah;Stock, James H.;Sun, Liyang
当工具与内生回归变量的相关性较弱时，传统的工具变量(IV)估计和推断方法变得不可靠。计量经济学中的大量文献已经开发出检测弱工具和构建稳健置信集的程序，但这篇文献中的许多结果仅限于具有独立和同方差数据的设置，而实践中遇到的数据经常违反这些假设。我们回顾了线性IV回归中弱工具的文献，重点介绍了非同方差(异方差、序列相关或聚类)数据的结果。为了评估弱工具的实际重要性，我们还基于2014年至2018年发表在《美国经济评论》上的一篇使用IV的论文的调查，报告了表格和模拟。这些结果表明，弱工具仍然是一个重要的实证实践问题，研究人员可以采取一些简单的步骤，在应用中更好地处理弱工具。
When instruments are weakly correlated with endogenous regressors, conventional methods for instrumental variables (IV) estimation and inference become unreliable. A large literature in econometrics has developed procedures for detecting weak instruments and constructing robust confidence sets, but many of the results in this literature are limited to settings with independent and homoskedastic data, while data encountered in practice frequently violate these assumptions. We review the literature on weak instruments in linear IV regression with an emphasis on results for nonhomoskedastic (heteroskedastic, serially correlated, or clustered) data. To assess the practical importance of weak instruments, we also report tabulations and simulations based on a survey of papers published in the American Economic Review from 2014 to 2018 that use IV. These results suggest that weak instruments remain an important issue for empirical practice, and that there are simple steps that researchers can take to better handle weak instruments in applications.