Inference with Many Weak Instruments

Anna Mikusheva and Liyang Sun

Papers from arXiv.org

Abstract: We develop a concept of weak identification in linear IV 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 (JIVE). This new pre-test is valid under heteroscedasticity and with many instruments.

Date: 2020-04, Revised 2021-10
New Economics Papers: this item is included in nep-ecm and nep-ore
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Journal Article: Inference with Many Weak Instruments (2022)
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