CFDP 1631

Inference for Parameters Defined by Moment Inequalities Using Generalized Moment Selection

Author(s): 

Publication Date: October 2007

Pages: 58

Abstract: 

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 Codes:  C12, C15

See CFP: 1291