期刊
JOURNAL OF ECONOMETRICS
卷 238, 期 2, 页码 -出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.jeconom.2023.105602
关键词
Many instruments; Power; Size; Weak identification
We propose a linear combination of jackknife tests for IV regressions with weak instruments and heteroskedasticity, and select the weights based on a decision-theoretic rule. Our test performs well in empirical applications and exhibits good power properties.
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.
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