On Bootstrap inconsistency and Bonferroni-based size-correction for the subset Anderson-Rubin test under conditional homoskedasticity - Wang, Wenjie (2020)
We focus on the linear instrumental variable model with two endogenous regressors under conditional homoskedasticity, and study the subset Anderson and Rubin (1949, AR) test when the nuisance structural parameter, the unrestricted slope coefficient of endogenous regressor, may be weakly identified. Weak identification leads to nonstandard null limiting distributions, and alternative to the usual chi-squared critical value is needed. We first investigate the bootstrap validity for the subset AR test based on various plug-in estimators, and show that the bootstrap provides asymptotic refinement when the nuisance structural parameter is strongly identified, but is inconsistent when it is weakly identified. This is in contrast to the result of bootstrap validity in Moreira et al. (2009). Then, we propose a Bonferroni-based size-correction method that yields correct asymptotic size for all the test statistics considered. The power performance of size-corrected tests can be further improved by applying the mapping between structural and endogenous parameters in the model. Monte Carlo experiments confirm the bootstrap inconsistency and demonstrate that all the subset tests based on our correction technique control the size.