Asymptotic Refinements of a Misspecification-Robust Bootstrap for GMM Estimators
Dr SeoJeong (Jay) Lee, Australian School of Business, UNSW
7th Sep 2012 11:00 am - Room 498 Merewether Building H04
I propose a nonparametric iid bootstrap that achieves asymptotic refinements for t tests and confidence intervals based on the generalized method of moments (GMM) estimators even when the model is misspecified. In addition, my bootstrap does not require recentering the bootstrap moment function, which has been considered as a critical procedure for bootstrapping GMM. Regardless of whether the assumed model is correctly specified or not, 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 procedure is to use a misspecification-robust variance estimator for GMM of Hall and Inoue (2003, Journal of Econometrics 114, 361-394) in constructing the t statistic. Examples of overidentified and possibly misspecified moment condition models with Monte Carlo simulation results are provided: (i) Combining data sets, and (ii) invalid instrumental variables.