Simple Local Polynomial Density Estimators

By M. D. Cattaneo, Michael Jansson, Xinwei Ma. Journal of the American Statistical Association, 2018

609 citations recorded by the source corpus.

Abstract This article introduces an intuitive and easy-to-implement nonparametric density estimator based on local polynomial techniques. The estimator is fully boundary adaptive and automatic, but does not require prebinning or any other transformation of the data. We study the main asymptotic properties of the estimator, and use these results to provide principled estimation, inference, and bandwidth selection methods. As a substantive application of our results, we develop a novel discontinuity in density testing procedure, an important problem in regression discontinuity designs and other program evaluation settings. An illustrative empirical application is given. Two companion Stata and R software packages are provided.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a1e9f36ea5149928d4063245315b8363d0b9172c

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Citations in the corpus

The Impact of Algorithms on Judicial Discretion : Evidence from Regression Discontinuities (2018, background): “Tests of the density around the thresholds (McCrary, 2008; Cattaneo et al., 2017) suggest it has not been manipulated.”
Testing continuity of a density via g-order statistics in the regression discontinuity design (2018, methodology): “As we mentioned in the introduction, related binomial tests have been recently presented in the RDD context by Cattaneo et al. (2016) and Cattaneo et al. (2017b). There, the authors use a binomial test based on the number of “successes” in a window Z ∈ [−h, h] for a varying bandwidth h. The authors ”
A Practical Introduction to Regression Discontinuity Designs (2019, background): “Cattaneo, Jansson, and Ma (2019) develop the local polynomial density estimator implemented in rddensity; see also Cattaneo, Jansson, and Ma (2018) for details on this statistical package and further numerical evidence.”
Decomposing Wage Distributions using Recentered Influence Function Regressions (2018, methodology): “(1993) and DiNardo et al. (1996) showed that inequality expanded all through the wage distribution during the 1980s.”