Conditional Inference with a Functional Nuisance Parameter

38 Pages Posted: 25 Sep 2014

See all articles by Isaiah Andrews

Isaiah Andrews

Harvard Society of Fellows

Anna Mikusheva

Massachusetts Institute of Technology (MIT) - Department of Economics

Date Written: September 22, 2014

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.

Keywords: weak identification, similar test, conditional inferences

JEL Classification: C30

Suggested Citation

Andrews, Isaiah and Mikusheva, Anna, Conditional Inference with a Functional Nuisance Parameter (September 22, 2014). MIT Department of Economics Working Paper No. 14-17, Available at SSRN: https://ssrn.com/abstract=2500534 or http://dx.doi.org/10.2139/ssrn.2500534

Isaiah Andrews

Harvard Society of Fellows ( email )

1875 Cambridge Street
Cambridge, MA 02138
United States

Anna Mikusheva (Contact Author)

Massachusetts Institute of Technology (MIT) - Department of Economics ( email )

50 Memorial Drive
E52-391
Cambridge, MA 02142
United States

HOME PAGE: http://econ-www.mit.edu/faculty/amikushe

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
78
Abstract Views
725
Rank
559,655
PlumX Metrics