Accurate and Robust Tests for Indirect Inference

Veronika Czellar and Elvezio Ronchetti
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Veronika Czellar: HEC Paris - Ecole des Hautes Etudes Commerciales
Elvezio Ronchetti: UNIGE - Université de Genève = University of Geneva

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Abstract: In this paper we propose accurate parameter and over-identification tests for indirect inference. Under the null hypothesis the new tests are asymptotically χ2-distributed with a relative error of order n−1. They exhibit better finite sample accuracy than classical tests for indirect inference, which have the same asymptotic distribution but an absolute error of order n−1/2. Robust versions of the tests are also provided. We illustrate their accuracy in nonlinear regression, Poisson regression with overdispersion and diffusion models.

Date: 2010-09-01
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Published in Biometrika, 2010, 97 (3), 621-630 p. ⟨10.1093/biomet/asq040⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02313230

DOI: 10.1093/biomet/asq040

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