A0730
Title: A conditional linear combination test with many weak instruments
Authors: Wenjie Wang - Nanyang Technological University (Singapore) [presenting]
Yichong Zhang - Singapore Management University (Singapore)
Dennis Lim - Singapore Management University (Singapore)
Abstract: A linear combination of jackknife Anderson-Rubin (AR), jackknife Lagrangian multiplier (LM), and orthogonalized jackknife LM tests are considered for inference in IV regressions with many weak instruments and heteroskedasticity. Following previous work, the weights in the linear combination are chosen based on a decision-theoretic rule that is adaptive to the identification strength. Under both weak and strong identifications, the proposed test controls the asymptotic size and is admissible among a certain class of tests. Under strong identification, the 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 confirm the good power properties of our test.
