Kernel Density and Local Polynomial Regression Methods

The package nprobust implements estimation, inference, bandwidth selection, and graphical procedures for kernel density and local polynomial regression methods, including robust bias-corrected confidence intervals.

• lprobust(): local polynomial point estimation and robust bias-corrected inference.
• lpbwselect(): data-driven bandwidth selection for local polynomial regression.
• kdrobust(): kernel density point estimation and robust bias-corrected inference.
• kdbwselect(): data-driven bandwidth selection for kernel density estimation.
• nprobust.plot(): graphical presentation of lprobust() and kdrobust() results.

See references for methodological and practical details.

Website: https://nppackages.github.io/.

Source code: https://github.com/nppackages/nprobust.

Authors

Sebastian Calonico (scalonico@ucdavis.edu)

Matias D. Cattaneo (matias.d.cattaneo@gmail.com)

Max H. Farrell (mhfarrell@gmail.com)

Installation

To install/update use R:

install.packages("nprobust")

Usage

library(nprobust)

# Cholesterol trial data used by the Python and Stata examples.
data <- read.csv("../nprobust_data.csv")
control <- data$t == 0

# Local polynomial regression with robust bias-corrected confidence intervals.
result <- lprobust(data$cholf[control], data$chol1[control])
summary(result)

# Data-driven bandwidth selection.
bw <- lpbwselect(data$cholf[control], data$chol1[control],
                 bwselect = "mse-dpi", neval = 7)
summary(bw)

# Kernel density estimation.
density <- kdrobust(data$chol1[control], neval = 30)
summary(density)

# Kernel density bandwidth selection.
summary(kdbwselect(data$chol1[control], bwselect = "imse-dpi"))

# Plot a local polynomial fit.
nprobust.plot(result, xlabel = "chol1", ylabel = "cholf")

• Replication: nprobust illustration, nprobust data.

Dependencies

• ggplot2

References

For overviews and introductions, see nppackages website.

Software and Implementation

• Calonico, Cattaneo and Farrell (2019): nprobust: Nonparametric Kernel-Based Estimation and Robust Bias-Corrected Inference.
  Journal of Statistical Software 91(8): 1-33.

Technical and Methodological

• Calonico, Cattaneo and Farrell (2018): On the Effect of Bias Estimation on Coverage Accuracy in Nonparametric Inference.
  Journal of the American Statistical Association 113(522): 767-779.

• Calonico, Cattaneo and Farrell (2022): Coverage Error Optimal Confidence Intervals for Local Polynomial Regression.
  Bernoulli 28(4): 2998-3022.