Asymptotic refinements of a misspecification-robust bootstrap for generalized method of moments estimators

Seojeong Lee

Journal of Econometrics, 2014, vol. 178, issue P3, 398-413

Abstract: 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.

Keywords: Nonparametric iid bootstrap; Asymptotic refinement; Edgeworth expansion; Generalized method of moments; Model misspecification (search for similar items in EconPapers)
JEL-codes: C14 C15 C31 C33 (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (16)

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Related works:
Working Paper: Asymptotic Refinements of a Misspecification-Robust Bootstrap for Generalized Method of Moments Estimators (2018)
Working Paper: Asymptotic Refinements of a Misspecification-Robust Bootstrap for Generalized Method of Moments Estimators (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:178:y:2014:i:p3:p:398-413

DOI: 10.1016/j.jeconom.2013.05.008

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