nprobust-pkg 1.0.0

Estimation, inference, bandwidth selection, and graphical procedures for kernel density and local polynomial regression methods.

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 ([e-mail])

Matias D. Cattaneo ([e-mail])

Max H. Farrell ([e-mail])

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.