Conditional Inference with a Functional Nuisance Parameter
Statistics Theory
2014-09-24 v1 Statistics Theory
Abstract
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 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.
Cite
@article{arxiv.1409.6337,
title = {Conditional Inference with a Functional Nuisance Parameter},
author = {Isaiah Andrews and Anna Mikusheva},
journal= {arXiv preprint arXiv:1409.6337},
year = {2014}
}