Skip to main content
eScholarship
Open Access Publications from the University of California

Simple Local Polynomial Density Estimators

  • Author(s): Cattaneo, Matias D
  • Jansson, Michael
  • Ma, Xinwei
  • et al.

Published Web Location

https://arxiv.org/abs/1811.11512
No data is associated with this publication.
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.

Many UC-authored scholarly publications are freely available on this site because of the UC Academic Senate's Open Access Policy. Let us know how this access is important for you.

Item not freely available? Link broken?
Report a problem accessing this item