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dc.contributor.authorLee, Sokbaeen_US
dc.contributor.authorSong, Kyungchulen_US
dc.contributor.authorWhang, Yoon-Jaeen_US
dc.description.abstractThis paper develops tests for inequality constraints of nonparametric regression functions. The test statistics involve a one-sided version of Lp-type functionals of kernel estimators. Drawing on the approach of Poissonization, this paper establishes that the tests are asymptotically distribution free, admitting asymptotic normal approximation. Furthermore, the tests have nontrivial local power against a certain class of local alternatives converging to the null at the rate of n-1/2. Some results from Monte Carlo simulations are presented.en_US
dc.publisherCentre for Microdata Methods and Practice (cemmap) Londonen_US
dc.relation.ispartofseriescemmap working paper CWP12/11en_US
dc.subject.keywordKernel estimationen_US
dc.subject.keywordone-sided testen_US
dc.subject.keywordlocal poweren_US
dc.subject.keywordLp normen_US
dc.titleTesting functional inequalitiesen_US
dc.typeWorking Paperen_US
Appears in Collections:cemmap working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies (IFS)

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