  I propose a nonparametric iid bootstrap that achieves asymptotic refinements
for t tests and confidence intervals based on GMM estimators even when the
model is misspecified. In addition, my bootstrap does not require recentering
the moment function, which has been considered as critical for GMM. Regardless
of model misspecification, the proposed bootstrap achieves the same sharp
magnitude of refinements as the conventional bootstrap methods which establish
asymptotic refinements by recentering in the absence of misspecification. The
key idea is to link the misspecified bootstrap moment condition to the large
sample theory of GMM under misspecification of Hall and Inoue (2003). Two
examples are provided: Combining data sets and invalid instrumental variables.
