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dc.contributor.authorChen, Le-Yuen_US
dc.contributor.authorSzroeter, Jerzyen_US
dc.date.accessioned2012-07-27en_US
dc.date.accessioned2012-10-16T13:08:57Z-
dc.date.available2012-10-16T13:08:57Z-
dc.date.issued2012en_US
dc.identifier.pidoi:10.1920/wp.cem.2012.1612en_US
dc.identifier.urihttp://hdl.handle.net/10419/64712-
dc.description.abstractThis paper proposes a class of origin-smooth approximators of indicators underlying the sum-of-negative-part statistic for testing multiple inequalities. The need for simulation or bootstrap to obtain test critical values is thereby obviated. A simple procedure is enabled using fixed critical values. The test is shown to have correct asymptotic size in the uniform sense that supremum finite-sample rejection probability over null-restricted data distributions tends asymptotically to nominal signficance level. This applies under weak assumptions allowing for estimator covariance singularity. The test is unbiased for a wide class of local alternatives. A new theorem establishes directions in which the test is locally most powerful. The proposed procedure is compared with predominant existing tests in structure, theory and simulation.en_US
dc.language.isoengen_US
dc.publisherCentre for Microdata Methods and Practice Londonen_US
dc.relation.ispartofseriescemmap working paper CWP16/12en_US
dc.subject.jelC1en_US
dc.subject.jelC4en_US
dc.subject.ddc330en_US
dc.subject.keywordTesten_US
dc.subject.keywordMultiple inequalitiesen_US
dc.subject.keywordOne-sided hypothesisen_US
dc.subject.keywordComposite nullen_US
dc.subject.keywordBinding constraintsen_US
dc.subject.keywordAsymptotic exactnessen_US
dc.subject.keywordCovariance singularityen_US
dc.subject.keywordIndicator smoothingen_US
dc.titleTesting multiple inequality hypotheses: A smoothed indicator approachen_US
dc.typeWorking Paperen_US
dc.identifier.ppn720226937en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US
Appears in Collections:cemmap working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies (IFS)

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