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Bootstrap inference for misspecified moment condition models

Author

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  • Mihai Giurcanu

    (University of Chicago)

  • Brett Presnell

    (University of Florida)

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.

Suggested Citation

  • Mihai Giurcanu & Brett Presnell, 2018. "Bootstrap inference for misspecified moment condition models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(3), pages 605-630, June.
    • Handle: RePEc:spr:aistmt:v:70:y:2018:i:3:d:10.1007_s10463-017-0604-2
    DOI: 10.1007/s10463-017-0604-2
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    References listed on IDEAS

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    1. Wang, Wenjie, 2020. "On the inconsistency of nonparametric bootstraps for the subvector Anderson–Rubin test," Economics Letters, Elsevier, vol. 191(C).

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