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Inference For Parameters Defined By Moment Inequalities Using Generalized Moment Selection, Donald W.K. Andrews, Patrik Guggenberger
Inference For Parameters Defined By Moment Inequalities Using Generalized Moment Selection, Donald W.K. Andrews, Patrik Guggenberger
Cowles Foundation Discussion Papers
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 …
Validity Of Subsampling And ‘Plug-In Asymptotic’ Inference For Parameters Defined By Moment Inequalities, Donald W.K. Andrews, Patrik Guggenberger
Validity Of Subsampling And ‘Plug-In Asymptotic’ Inference For Parameters Defined By Moment Inequalities, Donald W.K. Andrews, Patrik Guggenberger
Cowles Foundation Discussion Papers
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 + δ moments finite are required. The …