Simple Local Polynomial Density Estimators

Matias Cattaneo, Michael Jansson and Xinwei Ma

Department of Economics, Working Paper Series from Department of Economics, Institute for Business and Economic Research, UC Berkeley

Abstract: 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.

Keywords: Density estimation; Local polynomial methods; Manipulation test; Regression discontinuity; Statistics; Econometrics; Demography; Statistics & Probability (search for similar items in EconPapers)
Date: 2020-07-02
References: Add references at CitEc
Citations: View citations in EconPapers (225)

Downloads: (external link)
https://www.escholarship.org/uc/item/9vt997qn.pdf;origin=repeccitec (application/pdf)

Related works:
Journal Article: Simple Local Polynomial Density Estimators (2020)
Working Paper: Simple Local Polynomial Density Estimators (2020)
Working Paper: Simple Local Polynomial Density Estimators (2019)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cdl:econwp:qt9vt997qn

Access Statistics for this paper

More papers in Department of Economics, Working Paper Series from Department of Economics, Institute for Business and Economic Research, UC Berkeley Contact information at EDIRC.
Bibliographic data for series maintained by Lisa Schiff ().