Accurate and Robust Tests for Indirect Inference

V. CZELLAR, E. Ronchetti


September 2010, vol. 97, n°3, pp.621-630

Departments: Economics & Decision Sciences

accepté le 22/03/2010In this paper we propose accurate parameter and over-identification tests for indirect inference. Under the null hypothesis the new tests are asymptotically ?2-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. Author Keywords: Indirect inference; M-estimator; Nonlinear regression; Overdispersion; Parameter test; Robust estimator; Saddlepoint test; Sparsity; Test for over-identification