Simple Local Polynomial Density Estimators

• 成果类型: Article
• 署名作者: Cattaneo, Matias D.; Jansson, Michael; Ma, Xinwei
• 署名单位: Princeton University; CREATES; University of California System; University of California Berkeley; University of California System; University of California San Diego
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2019.1635480
• 发表日期: 2020
• 页码: 1449-1455
• 关键词: regression discontinuity designs; head-start; manipulation; inference

摘要

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.