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, 2021a).
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 (2021b) for more implementation details and illustrations.
R packages useful for nonparametric estimation and inference are
available at https://nppackages.github.io/.
Matias D. Cattaneo, Princeton University. firstname.lastname@example.org.
Michael Jansson, University of California Berkeley. email@example.com.
Xinwei Ma (maintainer), University of California San Diego. firstname.lastname@example.org.
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.
Calonico, S., M. D. Cattaneo, and M. H. Farrell. 2020. Coverage Error Optimal Confidence Intervals for Local Polynomial Regression. Working paper.
Cattaneo, M. D., M. Jansson, and X. Ma. 2020. Simple Local Polynomial Density Estimators. Journal of the American Statistical Association, 115(531): 1449-1455.
Cattaneo, M. D., M. Jansson, and X. Ma. 2021a. Local Regression Distribution Estimators. Journal of Econometrics, forthcoming.
Cattaneo, M. D., M. Jansson, and X. Ma. 2021b. lpdensity: Local Polynomial Density Estimation and Inference. Journal of Statistical Software, forthcoming.
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