Dennis Lim, Wenjie Wang, Yichong Zhang
arXiv 22 Jul 2022 · Econometrics · publishedJournal of Econometrics (2023) · 5 citations (OpenAlex)
arXiv:2207.11137 · PDF · DOI · OpenAlex · Extracted main text
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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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Andrews, I (2016) Conditional linear combination tests for weakly identified models | 0.956 | 24 | 9 | 88% |
| 2 | Angrist, J. D. and A. B. Krueger (1991) Does compulsory school attendance affect schooling and earning? | 0.935 | 11 | 5 | 82% |
| 3 | Matsushita, Y. and T. Otsu (2021) Jackknife empirical likelihood: small bandwidth, sparse network and high-dimensional asymptotics | 0.874 | 5 | 2 | 100% |
| 4 | Crudu, F., G. Mellace, and Z. Sándor (2021) Inference in instrumental variable models with heteroskedasticity and many instruments | 0.851 | 13 | 4 | 62% |
| 5 | Chao, J. C., N. R. Swanson, J. A. Hausman, W. K. Newey, and T. Woute… (2012) Asymptotic distribution of jive in a heteroskedastic iv regression with many instruments | 0.843 | 15 | 5 | 60% |
| 6 | Mikusheva, A. and L. Sun (2022) Inference with many weak instruments | 0.814 | 63 | 11 | 54% |
| 7 | Angrist, J. and B. Frandsen (2022) Machine labor | 0.811 | 4 | 2 | 100% |
| 8 | Kleibergen, F (2005) Kleibergen(2005)Testing parameters in GMM without assuming that they are identified | 0.811 | 4 | 2 | 100% |
| 9 | Matsushita, Y. and T. Otsu (2022) Jackknife lagrange multiplier test with many weak instruments | 0.811 | 4 | 2 | 100% |
| 10 | Stock, J. H. and J. H. Wright (2000) GMM with weak identification | 0.811 | 4 | 2 | 100% |
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