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Bootstrap inference for misspecified moment condition models

  • Mihai Giurcanu
  • Brett Presnell
Article

Abstract

We study the standard-bootstrap, the centered-bootstrap, and the empirical-likelihood bootstrap tests of hypotheses used in conjunction with generalized method of moments inference in correctly specified and misspecified moment condition models. We show that, under correct specification, the standard-bootstrap estimator of the null distribution of the J-test converges in distribution to a random distribution, verifying its inconsistency, while the centered and the empirical-likelihood bootstrap estimators are consistent. We provide higher-order expansions of the size distortions of the analytic and the bootstrap tests. We show that the standard-bootstrap parameter-tests are consistent under misspecification, while the centered-bootstrap parameter-tests are inconsistent. We propose a general bootstrap methodology which is highly accurate under correct specification and consistent under misspecification. In a simulation study, we explore the finite sample behavior of the analytic and the bootstrap tests for a panel data model and we apply our methodology on a real-world data set.

Keywords

GMM inference Standard-bootstrap Centered-bootstrap Empirical-likelihood bootstrap Edgeworth expansions Misspecified models 

Supplementary material

10463_2017_604_MOESM1_ESM.pdf (152 kb)
Supplementary material 1 (pdf 152 KB)

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Copyright information

© The Institute of Statistical Mathematics, Tokyo 2017

Authors and Affiliations

  1. 1.Department of Public Health SciencesUniversity of ChicagoChicagoUSA
  2. 2.Department of StatisticsUniversity of FloridaGainesvilleUSA

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