Bootstrap inference for misspecified moment condition models

Mihai Giurcanu () and Brett Presnell ()
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Mihai Giurcanu: University of Chicago
Brett Presnell: University of Florida

Annals of the Institute of Statistical Mathematics, 2018, vol. 70, issue 3, No 5, 605-630

Abstract: 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 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10463-017-0604-2

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