IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1906.06529.html
   My bibliography  Save this paper

lpdensity: Local Polynomial Density Estimation and Inference

Author

Listed:
  • Matias D. Cattaneo
  • Michael Jansson
  • Xinwei Ma

Abstract

Density estimation and inference methods are widely used in empirical work. When the data has compact support, as all empirical applications de facto do, conventional kernel-based density estimators are inapplicable near or at the boundary because of their well known boundary bias. Alternative smoothing methods are available to handle boundary points in density estimation, but they all require additional tuning parameter choices or other typically ad hoc modifications depending on the evaluation point and/or approach considered. This article discusses the R and Stata package lpdensity implementing a novel local polynomial density estimator proposed in Cattaneo, Jansson and Ma (2019), which is boundary adaptive, fully data-driven and automatic, and requires only the choice of one tuning parameter. The methods implemented also cover local polynomial estimation of the cumulative distribution function and density derivatives, as well as several other theoretical and methodological results. In addition to point estimation and graphical procedures, the package offers consistent variance estimators, mean squared error optimal bandwidth selection, and robust bias-corrected inference. A comparison with several other density estimation packages and functions available in R using a Monte Carlo experiment is provided.

Suggested Citation

  • Matias D. Cattaneo & Michael Jansson & Xinwei Ma, 2019. "lpdensity: Local Polynomial Density Estimation and Inference," Papers 1906.06529, arXiv.org.
  • Handle: RePEc:arx:papers:1906.06529
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1906.06529
    File Function: Latest version
    Download Restriction: no

    References listed on IDEAS

    as
    1. Cattaneo, Matias D & Jansson, Michael & Ma, Xinwei, 2019. "Simple Local Polynomial Density Estimators," University of California at San Diego, Economics Working Paper Series qt9vt997qn, Department of Economics, UC San Diego.
    2. Matias D. Cattaneo & Michael Jansson & Xinwei Ma, 2018. "Simple Local Polynomial Density Estimators," Papers 1811.11512, arXiv.org, revised Jun 2019.
    Full references (including those not matched with items on IDEAS)

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:1906.06529. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (arXiv administrators). General contact details of provider: http://arxiv.org/ .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.