Inference for Parameters Defined by Moment Inequalities Using Generalized Moment Selection
59 Pages Posted: 15 Oct 2007 Last revised: 21 Oct 2007
Date Written: October 2007
The topic of this paper is inference in models in which parameters are defined by moment inequalities and/or equalities. The parameters may or may not be identified. This paper introduces a new class of confidence sets and tests based on generalized moment selection (GMS). GMS procedures are shown to have correct asymptotic size in a uniform sense and are shown not to be asymptotically conservative.
The power of GMS tests is compared to that of subsampling, m out of n bootstrap, and plug-in asymptotic (PA) tests. The latter three procedures are the only general procedures in the literature that have been shown to have correct asymptotic size in a uniform sense for the moment inequality/equality model. GMS tests are shown to have asymptotic power that dominates that of subsampling, m out of n bootstrap, and PA tests. Subsampling and m out of n bootstrap tests are shown to have asymptotic power that dominates that of PA tests.
Keywords: Asymptotic size, Asymptotic power, Confidence set, Exact size, Generalized moment selection, m out of n bootstrap, Subsampling, Moment inequalities, Moment selection, Test
JEL Classification: C12, C15
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