Sample sensitivity for two-step and continuous updating GMM estimators

This paper follows up the sensitivity analysis by Andrews, Gentzkow and Shapiro (2017) for biases in GMM estimators due to local violations of identifying assumptions, and proposes complementary bias measures that are sensitive to different choices of GMM weight matrices by considering a specific form of the local perturbation. Our method accommodates the two-step and continuous updating GMM estimators with or without centering. The proposed bias measures are illustrated by a consumption based asset pricing model using Japanese data.