Inference with Many Weak Instruments (Joint with Liyang Sun)
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.

Link to paper: economics.mit.edu/files/19652

Please sign up for meetings here: docs.google.com/spreadsheets/d/1GRwPBmtpUwstC4fdLZrnxfnARNYHedHykoRZG4Xq2Bo/edit#gid=0
Date: 26 February 2021, 14:15 (Friday, 6th week, Hilary 2021)
Venue: Held on Zoom
Speaker: Anna Mikusheva (Massachusetts Institute of Technology)
Organising department: Department of Economics
Part of: Nuffield Econometrics Seminar
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Audience: Members of the University only
Editor: Melis Clark