Accurate and robust tests for indirect inference

• 成果类型: Article
• 署名作者: Czellar, Veronika; Ronchetti, Elvezio
• 署名单位: Hautes Etudes Commerciales (HEC) Paris; University of Geneva
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asq040
• 发表日期: 2010
• 页码: 621630
• 关键词: generalized-method; saddlepoint approximations; regression-models

摘要

In this paper we propose accurate parameter and over-identification tests for indirect inference. Under the null hypothesis the new tests are asymptotically chi(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.