Asymptotic Refinements of a Misspecification-Robust Bootstrap for GEL Estimators

Seojeong Lee

No 2014-02, Discussion Papers from School of Economics, The University of New South Wales

Abstract: I propose a nonparametric iid bootstrap procedure for the empirical likelihood, the exponential tilting, and the exponentially tilted empirical likelihood estimators that achieves sharp asymptotic refinements for t tests and confidence intervals based on such estimators. Furthermore, the proposed bootstrap is robust to model misspecification, i.e., it achieves asymptotic refinements regardless of whether the assumed moment condition model is correctly specified or not. This result is new, because asymptotic refinements of the bootstrap based on these estimators have not been established in the literature even under correct model specification. Monte Carlo experiments are conducted in dynamic panel data setting to support the theoretical finding. As an application, bootstrap confidence intervals for the returns to schooling of Hellerstein and Imbens (1999) are calculated. The returns to schooling may be higher.

Keywords: generalized empirical likelihood; bootstrap; asymptotic refinement; model misspecification (search for similar items in EconPapers)
JEL-codes: C14 C15 C31 C33 (search for similar items in EconPapers)
Pages: 56 pages
Date: 2014-01
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (2)

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Journal Article: Asymptotic refinements of a misspecification-robust bootstrap for GEL estimators (2016)
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