Simple Local Polynomial Density Estimators

Matias Cattaneo, Michael Jansson and Xinwei Ma

Journal of the American Statistical Association, 2020, vol. 115, issue 531, 1449-1455

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.

Date: 2020
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Citations: View citations in EconPapers (227)

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Related works:
Working Paper: Simple Local Polynomial Density Estimators (2020)
Working Paper: Simple Local Polynomial Density Estimators (2020)
Working Paper: Simple Local Polynomial Density Estimators (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlasa:v:115:y:2020:i:531:p:1449-1455

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DOI: 10.1080/01621459.2019.1635480

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