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Asymptotic Refinements of a Misspecification-Robust Bootstrap for Generalized Method of Moments Estimators

  • Seojeong Lee

    ()

    (School of Economics, the University of New South Wales)

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 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, Journal of Econometrics 114, 361-394). Examples of possibly misspecified moment condition models with Monte Carlo simulation results are provided: (i) Combining data sets, and (ii) invalid instrumental variables.

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File URL: http://research.economics.unsw.edu.au/RePEc/papers/2013-09.pdf
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Paper provided by School of Economics, The University of New South Wales in its series Discussion Papers with number 2013-09.

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Length: 44 pages
Date of creation: Sep 2013
Date of revision:
Handle: RePEc:swe:wpaper:2013-09
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