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Inference for Parameters Defined by Moment Inequalities Using Generalized Moment Selection

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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.

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File URL: http://cowles.econ.yale.edu/P/cd/d16a/d1631.pdf
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Bibliographic Info

Paper provided by Cowles Foundation for Research in Economics, Yale University in its series Cowles Foundation Discussion Papers with number 1631.

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Length: 58 pages
Date of creation: Oct 2007
Date of revision:
Publication status: Published in Econometrica (January 2010), 78(1): 119-157
Handle: RePEc:cwl:cwldpp:1631

Note: CFP 1291
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Web page: http://cowles.econ.yale.edu/
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Postal: Cowles Foundation, Yale University, Box 208281, New Haven, CT 06520-8281 USA

Related research

Keywords: Asymptotic size; Asymptotic power; Confidence set; Exact size; Generalized moment selection; m out of n bootstrap; Subsampling; Moment inequalities; Moment selection; Test;

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