  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.
