nprobust-package: Nonparametric Robust Estimation and Inference Methods using...

In nprobust: Nonparametric Robust Estimation and Inference Methods using Local Polynomial Regression and Kernel Density Estimation

Description

This package provides tools for data-driven statistical analysis using local polynomial regression (LPR) and kernel density estimation (KDE) methods as described in Calonico, Cattaneo and Farrell (2018): lprobust for local polynomial point estimation and robust bias-corrected inference, lpbwselect for local polynomial bandwidth selection, kdrobust for kernel density point estimation and robust bias-corrected inference, kdbwselect for kernel density bandwidth selection, and nprobust.plot for plotting results. The main methodological and numerical features of this package are described in Calonico, Cattaneo and Farrell (2019).

Details

Package:	nprobust
Type:	Package
Version:	0.4.0
Date:	2020-08-24
License:	GPL-2

Function for LPR estimation and inference: lprobust
Function for LPR bandwidth selection: lpbwselect
Function for KDE estimation and inference: kdrobust
Function for KDE bandwidth selection: kdbwselect
Function for graphical analysis: nprobust.plot

Author(s)

Sebastian Calonico, Columbia University, New York, NY. [e-mail].

Matias D. Cattaneo, Princeton University, Princeton, NJ. [e-mail].

Max H. Farrell, University of Chicago, Chicago, IL. [e-mail].

References

Calonico, S., M. D. Cattaneo, and M. H. Farrell. 2018. On the Effect of Bias Estimation on Coverage Accuracy in Nonparametric Inference. Journal of the American Statistical Association, 113(522): 767-779. doi: 10.1080/01621459.2017.1285776.

Calonico, S., M. D. Cattaneo, and M. H. Farrell. 2019. nprobust: Nonparametric Kernel-Based Estimation and Robust Bias-Corrected Inference. Journal of Statistical Software, 91(8): 1-33. doi: 10.18637/jss.v091.i08.

nprobust documentation built on Aug. 26, 2020, 5:12 p.m.