lpdensity: Local Polynomial Density Estimation and Inference

lpdensity: Local Polynomial Density Estimation and Inference

: kernel-based nonparametrics, local polynomial, density estimation, bandwidth selection, bias correction, robust inference, boundary carpentry, R, Stata

Density estimation and inference methods are widely used in empirical work. When
the underlying distribution has compact support, conventional kernel-based density estimators are no longer consistent near or at the boundary because of their well-known
boundary bias. Alternative smoothing methods are available to handle boundary points
in density estimation, but they all require additional tuning parameter choices or other
typically ad hoc modifications depending on the evaluation point and/or approach considered. This article discusses the R and Stata package lpdensity implementing a novel
local polynomial density estimator proposed and studied in Cattaneo, Jansson, and Ma
(2020, 2022), which is boundary adaptive and involves only one tuning parameter. The
methods implemented also cover local polynomial estimation of the cumulative distribution function and density derivatives. In addition to point estimation and graphical
procedures, the package offers consistent variance estimators, mean squared error optimal
bandwidth selection, robust bias-corrected inference, and confidence bands construction,
among other features. A comparison with other density estimation packages available in
R using a Monte Carlo experiment is provided.

Matias D. Cattaneo

https://www.jstatsoft.org/article/view/v101i02

Princeton University

January 2022

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