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Inference for Parameters Defined by Moment Inequalities Using Generalized Moment Selection

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Author Info
Donald W.K. Andrews () (Cowles Foundation, Yale University)
Gustavo Soares (Dept. of Economics, Yale University)
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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Publisher Info
Paper provided by Cowles Foundation, 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:
Handle: RePEc:cwl:cwldpp:1631

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Postal: Cowles Foundation, Yale University, Box 208281, New Haven, CT 06520-8281 USA

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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

Find related papers by JEL classification:
C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods

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