Accurate and Robust Tests for Indirect Inference

Veronika Czellar () and Elvezio Ronchetti
Additional contact information
Veronika Czellar: GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - HEC Paris - Ecole des Hautes Etudes Commerciales - CNRS - Centre National de la Recherche Scientifique
Elvezio Ronchetti: Department of Econometrics - UNIGE - Université de Genève = University of Geneva

Post-Print from HAL

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 (search for similar items in EconPapers)
Date: 2010-09
References: Add references at CitEc
Citations: View citations in EconPapers (9)

Published in Biometrika, 2010, 97 (3), pp.621-630. ⟨10.1093/biomet/asq040⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-00585938

DOI: 10.1093/biomet/asq040

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().