Inference for Parameters Defined by Moment Inequalities Using Generalized Moment Selection

Econometrica · 2010 · Volume 78 · Issue 1

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,mout ofnbootstrap, 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,mout ofnbootstrap, and PA tests. Subsampling andmout ofnbootstrap 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, mout ofnbootstrap, subsampling, moment inequalities, moment selection, test

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