Validity of Subsampling and "Plug-in Asymptotic" Inference for Parameters Defined by Moment Inequalities

Donald W. K. Andrews () and Patrik Guggenberger
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Patrik Guggenberger: Dept. of Economics, UCLA

No 1620, Cowles Foundation Discussion Papers from Cowles Foundation, Yale University

Abstract: This paper considers inference for parameters defined by moment inequalities and equalities. The parameters need not be identified. For a specified class of test statistics, this paper establishes the uniform asymptotic validity of subsampling, m out of n bootstrap, and "plug-in asymptotic" tests and confidence intervals for such parameters. Establishing uniform asymptotic validity is crucial in moment inequality problems because the test statistics of interest have discontinuities in their pointwise asymptotic distributions. The size results are quite general because they hold without specifying the particular form of the moment conditions -- only 2+delta moments finite are required. The results allow for i.i.d. and dependent observations and for preliminary consistent estimation of identified parameters.

Keywords: Asymptotic size; Confidence set; Exact size; m out of n bootstrap; Subsampling; Moment inequalities (search for similar items in EconPapers)
JEL-codes: C12 C15 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm
Date: Written
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Related works:
Journal Article: VALIDITY OF SUBSAMPLING AND ?PLUG-IN ASYMPTOTIC? INFERENCE FOR PARAMETERS DEFINED BY MOMENT INEQUALITIES (2009)
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