ON THE ASYMPTOTIC SIZE DISTORTION OF TESTS WHEN INSTRUMENTS LOCALLY VIOLATE THE EXOGENEITY ASSUMPTION
工具变量局部违反外生性时检验的渐近尺寸失真AI 解读 ↓
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工具变量局部违反外生性时检验的渐近尺寸失真
- 研究问题
- 在线性工具变量模型中，若工具可能较弱且局部违反外生性，不同检验的实际拒绝概率会如何偏离名义水平？
- 方法与思路
- 作者推导多类工具变量检验及其广义经验似然版本的渐近尺寸，并比较恰好识别、过度识别及不同参数序列。
- 材料陈述的贡献
- 摘要称，恰好识别时所考察检验同样失真；过度识别下存在有利于AR型检验的比较，但也有其更易过度拒绝的序列。
- 阅读关注点
- 应核对外生性局部偏离与工具强度的联合设定，尤其比较结论所适用的参数序列，以及尺寸失真的衡量方式。
- 阅读边界
- 仅据所给Crossref摘要，不能断言某类检验普遍更稳健；摘要已指出比较存在例外，具体失真幅度和应用条件未说明。
原文摘要
In the linear instrumental variables model with possibly weak instruments we derive the asymptotic size of testing procedures when instruments locally violate the exogeneity assumption. We study the tests by Anderson and Rubin (1949, The Annals of Mathematical Statistics 20, 46–63), Moreira (2003, Econometrica 71, 1027–1048), and Kleibergen (2005, Econometrica 73, 1103–1123) and their generalized empirical likelihood versions. These tests have asymptotic size equal to nominal size when the instruments are exogenous but are size distorted otherwise. While in just-identified models all the tests that we consider are equally size-distorted asymptotically, the Anderson-Rubin type tests are less size-distorted than the tests of Moreira (2003) and Kleibergen in over-identified situations. On the other hand, we also show that there are parameter sequences under which the former test asymptotically overrejects more frequently. Given that strict exogeneity of instruments is often a questionable assumption, our findings should be important to applied researchers who are concerned about the degree of size distortion of their inference procedure. We suggest robustness of asymptotic size under local model violations as a new alternative measure to choose among competing testing procedures. We also investigate the subsampling and hybrid tests introduced in Andrews and Guggenberger (2010a, Journal of Econometrics 158, 285–305) and show that they do not offer any improvement in terms of size-distortion reduction over the Anderson-Rubin type tests.