  We consider estimation in moment condition models and show that under any
bound on identification strength, asymptotically admissible (i.e. undominated)
estimators in a wide class of estimation problems must be uniformly continuous
in the sample moment function. GMM estimators are in general discontinuous in
the sample moments, and are thus inadmissible. We show, by contrast, that
bagged, or bootstrap aggregated, GMM estimators as well as quasi-Bayes
posterior means have superior continuity properties, while results in the
literature imply that they are equivalent to GMM when identification is strong.
In simulations calibrated to published instrumental variables specifications,
we find that these alternatives often outperform GMM.
