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

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

Domains: Economy and decision science

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