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Accurate and Robust Tests for Indirect Inference

Veronika Czellar 1 Elvezio Ronchetti 2
1 GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC
2 Department of Econometrics
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.
Keywords : Indirect inference M-estimator Nonlinear regression Overdispersion Parameter test Robust estimator Saddlepoint test Sparsity Test for over-identification
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https://hal-hec.archives-ouvertes.fr/hal-00585938
Contributor : Antoine Haldemann <>
Submitted on : Thursday, April 14, 2011 - 11:33:08 AM
Last modification on : Thursday, January 11, 2018 - 6:19:32 AM

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Veronika Czellar, Elvezio Ronchetti. Accurate and Robust Tests for Indirect Inference. Biometrika, Oxford University Press (OUP), 2010, 97 (3), pp.621-630. ⟨10.1093/biomet/asq040⟩. ⟨hal-00585938⟩

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