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Simple Local Polynomial Density Estimators

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  • Matias D. Cattaneo
  • Michael Jansson
  • Xinwei Ma

Abstract

This paper 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 pre-binning 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.

Suggested Citation

  • Matias D. Cattaneo & Michael Jansson & Xinwei Ma, 2018. "Simple Local Polynomial Density Estimators," Papers 1811.11512, arXiv.org, revised Jun 2019.
    • Handle: RePEc:arx:papers:1811.11512
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    References listed on IDEAS

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    1. Sebastian Calonico & Matias D. Cattaneo & Max H. Farrell, 2018. "On the Effect of Bias Estimation on Coverage Accuracy in Nonparametric Inference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(522), pages 767-779, April.
    2. McCrary, Justin, 2008. "Manipulation of the running variable in the regression discontinuity design: A density test," Journal of Econometrics, Elsevier, vol. 142(2), pages 698-714, February.
    3. Jens Ludwig & Douglas L. Miller, 2007. "Does Head Start Improve Children's Life Chances? Evidence from a Regression Discontinuity Design," The Quarterly Journal of Economics, Oxford University Press, vol. 122(1), pages 159-208.
    4. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, December.
    5. Matias D. Cattaneo & Juan Carlos Escanciano (ed.), 2017. "Regression Discontinuity Designs," Advances in Econometrics, Emerald Publishing Ltd, volume 38, number aeco.2017.38.
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