lpdensity-package {lpdensity}

lpdensity: Local Polynomial Density Estimation and Inference

Description

Without imposing stringent distributional assumptions or shape restrictions, nonparametric estimation has been popular in economics and other social sciences for counterfactual analysis, program evaluation, and policy recommendations. This package implements a novel density (and derivatives) estimator based on local polynomial regressions, documented in Cattaneo, Jansson and Ma (2020, 2023).

lpdensity implements the local polynomial regression based density (and derivatives) estimator. Robust bias-corrected inference methods, both pointwise (confidence intervals) and uniform (confidence bands), are also implemented. lpbwdensity implements the bandwidth selection methods. See Cattaneo, Jansson and Ma (2022) for more implementation details and illustrations.

Related Stata and R packages useful for nonparametric estimation and inference are available at https://nppackages.github.io/.

Author(s)

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

Michael Jansson, University of California Berkeley. [e-mail].

Xinwei Ma (maintainer), University of California San Diego. [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. 2022. Coverage Error Optimal Confidence Intervals for Local Polynomial Regression. Bernoulli, 28(4): 2998-3022. doi:10.3150/21-BEJ1445

Cattaneo, M. D., M. Jansson, and X. Ma. 2020. Simple Local Polynomial Density Estimators. Journal of the American Statistical Association, 115(531): 1449-1455. doi:10.1080/01621459.2019.1635480

Cattaneo, M. D., M. Jansson, and X. Ma. 2022. lpdensity: Local Polynomial Density Estimation and Inference. Journal of Statistical Software, 101(2), 1–25. doi:10.18637/jss.v101.i02

Cattaneo, M. D., M. Jansson, and X. Ma. 2023. Local Regression Distribution Estimators. Journal of Econometrics, forthcoming. doi:10.1016/j.jeconom.2021.01.006