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From the help desk: Local polynomial regression and Stata plugins

Author

Listed:
  • Roberto G. Gutierrez

    (StataCorp LP)

  • Jean Marie Linhart

    (StataCorp LP)

  • Jeffrey S. Pitblado

    (StataCorp LP)

Abstract

Local polynomial regression is a generalization of local mean smoothing as described by Nadaraya (1964)andWat s on (1964). Instead of fitting a local mean, one instead fits a local pth-order polynomial. Calculations for local polynomial regression are naturally more complex than those for local means, but local polynomial smooths have better statistical properties. The computational complexity may, however, be alleviated by using a Stata plugin. In this article, we describe the locpoly command for performing local polynomial regression. The calculations involved are implemented in both ado-code and with a plugin, allowing the user to assess the speed improvement obtained from using the plugin. Source code for the plugin is also provided as part of the package for this program. Copyright 2003 by StataCorp LP.

Suggested Citation

  • Roberto G. Gutierrez & Jean Marie Linhart & Jeffrey S. Pitblado, 2003. "From the help desk: Local polynomial regression and Stata plugins," Stata Journal, StataCorp LP, vol. 3(4), pages 412-419, December.
    • Handle: RePEc:tsj:stataj:v:3:y:2003:i:4:p:412-419
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    Cited by:

    1. Beneito, Pilar & Rochina-Barrachina, María Engracia & Sanchis, Amparo, 2014. "Learning through experience in Research & Development: An empirical analysis with Spanish firms," Technological Forecasting and Social Change, Elsevier, vol. 88(C), pages 290-305.
    2. Natalie Nitsche & Ansgar Hudde, 2022. "Countries embracing maternal employment opened schools sooner after Covid-19 lockdowns," MPIDR Working Papers WP-2022-008, Max Planck Institute for Demographic Research, Rostock, Germany.
    3. Hudde, Ansgar, 2022. "The unequal cycling boom in Germany," Journal of Transport Geography, Elsevier, vol. 98(C).
    4. Simon Berset & Martin Huber & Mark Schelker, 2023. "The fiscal response to revenue shocks," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 30(3), pages 814-848, June.
    5. Rik Chakraborti & Gavin Roberts, 2023. "How price-gouging regulation undermined COVID-19 mitigation: county-level evidence of unintended consequences," Public Choice, Springer, vol. 196(1), pages 51-83, July.
    6. Austin Nichols, 2007. "Causal inference with observational data," Stata Journal, StataCorp LP, vol. 7(4), pages 507-541, December.
    7. Ansgar Hudde & Marita Jacob, 2023. "There’s More in the Data! Using Month-Specific Information to Estimate Changes Before and After Major Life Events," SOEPpapers on Multidisciplinary Panel Data Research 1184, DIW Berlin, The German Socio-Economic Panel (SOEP).
    8. Hudde, Ansgar & Jacob, Marita, 2022. "There’s More in the Data! Using Month-Specific Information to Estimate Changes Before and After Major Life Events," SocArXiv vueas, Center for Open Science.
    9. Hudde, Ansgar, 2023. "It's the mobility culture, stupid! Winter conditions strongly reduce bicycle usage in German cities, but not in Dutch ones," Journal of Transport Geography, Elsevier, vol. 106(C).
    10. Shewmake, Sharon & Jarvis, Lovell, 2014. "Hybrid cars and HOV lanes," Transportation Research Part A: Policy and Practice, Elsevier, vol. 67(C), pages 304-319.
    11. Carrasco, Bruno & Mukhopadhyay, Hiranya, 2014. "Reserve Bank of India’s Policy Dilemmas: Reconciling Policy Goals in Times of Turbulence," ADB Economics Working Paper Series 393, Asian Development Bank.
    12. Hazhir Rahmandad, 2014. "Human Growth and Body Weight Dynamics: An Integrative Systems Model," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-22, December.

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