On the Asymptotic Sizes of Subset Anderson-Rubin and Lagrange Multiplier Tests in Linear Instrumental Variables Regression

• 成果类型: Article
• 署名作者: Guggenberger, Patrik; Kleibergen, Frank; Mavroeidis, Sophocles; Chen, Linchun
• 署名单位: University of California System; University of California San Diego; Brown University; University of Oxford
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA8953
• 发表日期: 2012
• 页码: 2649-2666
• 关键词: STRUCTURAL PARAMETERS; weak; statistics; inference; models; gmm

摘要

We consider tests of a simple null hypothesis on a subset of the coefficients of the exogenous and endogenous regressors in a single-equation linear instrumental variables regression model with potentially weak identification. Existing methods of subset inference (i) rely on the assumption that the parameters not under test are strongly identified, or (ii) are based on projection-type arguments. We show that, under homoskedasticity, the subset Anderson and Rubin (1949) test that replaces unknown parameters by limited information maximum likelihood estimates has correct asymptotic size without imposing additional identification assumptions, but that the corresponding subset Lagrange multiplier test is size distorted asymptotically.