CONDITIONAL INFERENCE WITH A FUNCTIONAL NUISANCE PARAMETER

• 成果类型: Article
• 署名作者: Andrews, Isaiah; Mikusheva, Anna
• 署名单位: Harvard University; Massachusetts Institute of Technology (MIT)
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA12868
• 发表日期: 2016
• 页码: 1571-1612
• 关键词: instrumental variables regression; weak instruments; quantile regression; models; tests; identification; gmm; integration; estimators; simulation

摘要

This paper shows that the problem of testing hypotheses in moment condition models without any assumptions about identification may be considered as a problem of testing with an infinite-dimensional nuisance parameter. We introduce a sufficient statistic for this nuisance parameter in a Gaussian problem and propose conditional tests. These conditional tests have uniformly correct asymptotic size for a large class of models and test statistics. We apply our approach to construct tests based on quasi-likelihood ratio statistics, which we show are efficient in strongly identified models and perform well relative to existing alternatives in two examples.