nprobust: Nonparametric Kernel-Based Estimation and Robust Bias-Corrected Inference

doi:10.18637/jss.v091.i08

nprobust: Nonparametric Kernel-Based Estimation and Robust Bias-Corrected Inference

2019-01-01

12

OA

AI

DOI：10.18637/jss.v091.i08

Original

Original request for help

Save

Abstract

En 中文

Nonparametric kernel density and local polynomial regression estimators are very popular in statistics, economics, and many other disciplines. They are routinely employed in applied work, either as part of the main empirical analysis or as a preliminary ingredient entering some other estimation or inference procedure. This article describes the main methodological and numerical features of the software package nprobust, which offers an array of estimation and inference procedures for nonparametric kernel-based density and local polynomial regression methods, implemented in both the R and Stata statistical platforms. The package includes not only classical bandwidth selection, estimation, and inference methods (Wand and Jones 1995; Fan and Gijbels 1996), but also other recent developments in the statistics and econometrics literatures such as robust bias-corrected inference and coverage error optimal bandwidth selection (Calonico, Cattaneo, and Farrell 2018, 2019a). Furthermore, this article also proposes a simple way of estimating optimal bandwidths in practice that always delivers the optimal mean square error convergence rate regardless of the specific evaluation point, that is, no matter whether it is implemented at a boundary or interior point. Numerical performance is illustrated using an empirical application and simulated data, where a detailed numerical comparison with other R packages is given.

Keywords:

kernel-based nonparametrics

bandwidth selection

bias correction

robust inference

R

Stata

nprobust: Nonparametric Kernel-Based Estimation and Robust Bias-Corrected Inference

Abstract

Journal

Organization