A conditional linear combination test with many weak instruments

Dennis Lim, Wenjie Wang and Yichong Zhang

Journal of Econometrics, 2024, vol. 238, issue 2

Abstract: 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.

Keywords: Many instruments; Power; Size; Weak identification (search for similar items in EconPapers)
JEL-codes: C12 C36 C55 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:238:y:2024:i:2:s0304407623003184

DOI: 10.1016/j.jeconom.2023.105602

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