Simple Local Polynomial Density Estimators

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.

[1]  Ivan Jeliazkov,et al.  Regression Discontinuity Designs: Theory and Applications , 2017 .

[2]  Guido Imbens,et al.  Why High-Order Polynomials Should Not Be Used in Regression Discontinuity Designs , 2014 .

[3]  Hidehiko Ichimura,et al.  Simultaneous selection of optimal bandwidths for the sharp regression discontinuity estimator , 2014, 1407.7697.

[4]  W. Newey,et al.  Large sample estimation and hypothesis testing , 1986 .

[5]  Matias D Cattaneo,et al.  Comparing Inference Approaches for RD Designs: a Reexamination of the Effect of Head Start on Child Mortality. , 2017, Journal of policy analysis and management : [the journal of the Association for Public Policy Analysis and Management].

[6]  J. Angrist,et al.  Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings , 1999 .

[7]  R. Carter Hill Regression Discontinuity Designs , 2017 .

[8]  Matias D. Cattaneo,et al.  lpdensity: Local Polynomial Density Estimation and Inference , 2019, J. Stat. Softw..

[9]  Hidehiko Ichimura,et al.  Simultaneous selection of optimal bandwidths for the sharp regression discontinuity estimator: Simultaneous selection of optimal bandwidths , 2018 .

[10]  Simon Jäger,et al.  A Permutation Test for the Regression Kink Design , 2018 .

[11]  Matias D. Cattaneo,et al.  Optimal Data-Driven Regression Discontinuity Plots , 2015 .

[12]  D. Rubin,et al.  Causal Inference for Statistics, Social, and Biomedical Sciences: A General Method for Estimating Sampling Variances for Standard Estimators for Average Causal Effects , 2015 .

[13]  Brigham R. Frandsen,et al.  Testing for Manipulation in the Regression Discontinuity Design When the Running Variable Is Discrete , 2016 .

[14]  Justin McCrary,et al.  Manipulation of the Running Variable in the Regression Discontinuity Design: A Density Test , 2007 .

[15]  Hidehiko Ichimura,et al.  Implementing Nonparametric and Semiparametric Estimators , 2006 .

[16]  Douglas L. Miller,et al.  Does Head Start Improve Children's Life Chances? Evidence from a Regression Discontinuity Design , 2005, SSRN Electronic Journal.

[17]  Jianqing Fan,et al.  Local polynomial modelling and its applications , 1994 .

[18]  T. Kitagawa A Test for Instrument Validity , 2015 .

[19]  J. Pearl,et al.  Bounds on Treatment Effects from Studies with Imperfect Compliance , 1997 .

[20]  Rohana J. Karunamuni,et al.  On boundary correction in kernel density estimation , 2005 .

[21]  Jianqing Fan Local Polynomial Modelling and Its Applications: Monographs on Statistics and Applied Probability 66 , 1996 .

[22]  Rohana J. Karunamuni,et al.  On kernel density estimation near endpoints , 1998 .

[23]  Sebastian Calonico,et al.  Robust Nonparametric Confidence Intervals for Regression‐Discontinuity Designs , 2014 .

[24]  W. Newey,et al.  Kernel Estimation of Partial Means and a General Variance Estimator , 1994, Econometric Theory.

[25]  Jianqing Fan,et al.  On automatic boundary corrections , 1997 .

[26]  Max H. Farrell,et al.  On the Effect of Bias Estimation on Coverage Accuracy in Nonparametric Inference , 2015, Journal of the American Statistical Association.

[27]  Ke-Li Xu,et al.  Estimation and Inference of Discontinuity in Density , 2013 .

[28]  Matias D. Cattaneo,et al.  Econometric Methods for Program Evaluation , 2018, Annual Review of Economics.

[29]  Ker-Chau Li,et al.  Asymptotic Optimality for $C_p, C_L$, Cross-Validation and Generalized Cross-Validation: Discrete Index Set , 1987 .

[30]  Otto Toivanen,et al.  When does regression discontinuity design work? Evidence from random election outcomes , 2018 .

[31]  Matthew P. Wand,et al.  Kernel Smoothing , 1995 .

[32]  Ying-Ying Lee,et al.  Regression Discontinuity Designs With a Continuous Treatment , 2019, Journal of the American Statistical Association.

[33]  Matias D. Cattaneo,et al.  Manipulation Testing Based on Density Discontinuity , 2018 .

[34]  David S. Lee,et al.  Regression Discontinuity Designs in Economics , 2009 .

[35]  Alberto Abadie Semiparametric instrumental variable estimation of treatment response models , 2003 .