论文标题
有条件的线性组合测试，带有许多弱仪器
A Conditional Linear Combination Test with Many Weak Instruments
论文作者
论文摘要
我们考虑了折刀安德森·罗宾（AR），折刀拉格朗日乘数（LM）和正交夹克刀LM测试的线性组合，以通过许多弱仪器和异性疾病进行IV回归中的推断。在I.Andrews（2016）之后，我们基于对识别强度的决策理论规则选择线性组合中的权重。在弱和强鉴定下，提出的测试控制渐近大小，在某些类别的测试中也可以接受。在强烈的识别下，我们的线性组合测试具有最佳的功能，可以根据基于折刀AR和LM测试构建的一类不变或无偏测试中的局部替代方案。对Angrist和Krueger（1991）的数据集的模拟和经验应用证实了我们测试的良好功能。
We consider a linear combination of jackknife Anderson-Rubin (AR), jackknife Lagrangian multiplier (LM), and orthogonalized jackknife LM tests for inference in IV regressions with many weak instruments and heteroskedasticity. Following I.Andrews (2016), we choose the weights in the linear combination based on a decision-theoretic rule that is adaptive to the identification strength. Under both weak and strong identifications, the proposed test controls asymptotic size and is admissible among certain class of tests. Under strong identification, our linear combination test has optimal power against local alternatives among the class of invariant or unbiased tests which are constructed based on jackknife AR and LM tests. Simulations and an empirical application to Angrist and Krueger's (1991) dataset confirm the good power properties of our test.
