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- handle: 10670/1.hg9syd
- hal: hal-00585938
- doi: 10.1093/biomet/asq040
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info:eu-repo/semantics/altIdentifier/doi/10.1093/biomet/asq040
Mots-clés
Indirect inference M-estimator Nonlinear regression Overdispersion Parameter test Robust estimator Saddlepoint test Sparsity Test for over-identificationSujets proches
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Veronika Czellar et al., « Accurate and Robust Tests for Indirect Inference », HAL-SHS : économie et finance, ID : 10.1093/biomet/asq040
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Résumé
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.