Inference with Many Weak Instruments
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2004.12445.pdf
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Submitted version
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387.72 KB
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1727b9e95a7286de6aa4c554601085ec
Author(s) •
Mikusheva, Anna
Sun, Liyang
Date Issued
2021
Journal
Review of Economic Studies
Publisher
Oxford University Press (OUP)
Citation
Mikusheva, Anna and Sun, Liyang. 2021. "Inference with Many Weak Instruments." Review of Economic Studies.
Version
Original manuscript
Abstract
Abstract
We develop a concept of weak identification in linear instrumental variable models in which the number of instruments can grow at the same rate or slower than the sample size. We propose a jackknifed version of the classical weak identification-robust Anderson–Rubin (AR) test statistic. Large-sample inference based on the jackknifed AR is valid under heteroscedasticity and weak identification. The feasible version of this statistic uses a novel variance estimator. The test has uniformly correct size and good power properties. We also develop a pre-test for weak identification that is related to the size property of a Wald test based on the Jackknife Instrumental Variable Estimator. This new pre-test is valid under heteroscedasticity and with many instruments.
We develop a concept of weak identification in linear instrumental variable models in which the number of instruments can grow at the same rate or slower than the sample size. We propose a jackknifed version of the classical weak identification-robust Anderson–Rubin (AR) test statistic. Large-sample inference based on the jackknifed AR is valid under heteroscedasticity and weak identification. The feasible version of this statistic uses a novel variance estimator. The test has uniformly correct size and good power properties. We also develop a pre-test for weak identification that is related to the size property of a Wald test based on the Jackknife Instrumental Variable Estimator. This new pre-test is valid under heteroscedasticity and with many instruments.
MIT Department
Massachusetts Institute of Technology. Department of Economics
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DOI of Published Version
https://doi.org/10.1093/RESTUD/RDAB097