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

Patrik Guggenberger, Frank Kleibergen, Sophocles Mavroeidis, Linchun Chen

Research output: Contribution to journalArticle

25 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)2649-2666
Number of pages18
JournalEconometrica
Volume80
Issue number6
DOIs
StatePublished - Nov 1 2012

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics

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