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Simple Local Polynomial Density Estimators

Author(s): Cattaneo, Matias D; Jansson, M; Ma, X

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dc.contributor.authorCattaneo, Matias D-
dc.contributor.authorJansson, M-
dc.contributor.authorMa, X-
dc.date.accessioned2021-10-11T14:17:34Z-
dc.date.available2021-10-11T14:17:34Z-
dc.date.issued2019-01-01en_US
dc.identifier.citationCattaneo, MD, Jansson, M, Ma, X. (2019). Simple Local Polynomial Density Estimators. Journal of the American Statistical Association, 10.1080/01621459.2019.1635480en_US
dc.identifier.issn0162-1459-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr1r877-
dc.description.abstract© 2019, © 2019 American Statistical Association. This article introduces an intuitive and easy-to-implement nonparametric density estimator based on local polynomial techniques. The estimator is fully boundary adaptive and automatic, but does not require prebinning or any other transformation of the data. We study the main asymptotic properties of the estimator, and use these results to provide principled estimation, inference, and bandwidth selection methods. As a substantive application of our results, we develop a novel discontinuity in density testing procedure, an important problem in regression discontinuity designs and other program evaluation settings. An illustrative empirical application is given. Two companion Stata and R software packages are provided.en_US
dc.language.isoen_USen_US
dc.relation.ispartofJournal of the American Statistical Associationen_US
dc.rightsAuthor's manuscripten_US
dc.titleSimple Local Polynomial Density Estimatorsen_US
dc.typeJournal Articleen_US
dc.identifier.doidoi:10.1080/01621459.2019.1635480-
dc.identifier.eissn1537-274X-
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/journal-articleen_US

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