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.

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 pip:

pip install nprobust_pkg

Usage

from pathlib import Path

import pandas as pd
from nprobust import kdrobust, kdbwselect, lprobust, lpbwselect, plot_lprobust

# Cholesterol trial data used by the R and Stata examples.
data = pd.read_csv(Path("..") / "nprobust_data.csv")
control = data["t"] == 0

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

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

# Kernel density estimation.
density = kdrobust(data.loc[control, "chol1"], neval=30)
print(density.summary())

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

# Plot a local polynomial fit.
fig = plot_lprobust(result, xlabel="chol1", ylabel="cholf")
fig.savefig("fit.png")

• Replication: nprobust illustration, nprobust data.

Dependencies

• numpy
• pandas
• scipy
• matplotlib (optional plotting extra)

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.