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Robinson's square root of N consistent semiparametric regression estimator in Stata

Author

Listed:
  • Vincenzo Verardi

    (University of Namur, FNRS
    Université Libre de Bruxelles)

  • Nicolas Debarsy

    (University of Namur
    Université d'Orléans)

Abstract

In this article, we describe http://www.stata-journal.com/software/Robinson’s (1988, Econometrica 56: 931– 954) double residual semiparametric regression estimator and H ̈ardle and Mam- men’s (1993, Annals of Statistics 21: 1926–1947) specification test implementation in Stata. We use some simple simulations to illustrate how this newly coded estima- tor outperforms the already available semiparametric plreg command (Lokshin, 2006, Stata Journal 6: 377–383).

Suggested Citation

  • Vincenzo Verardi & Nicolas Debarsy, 2012. "Robinson's square root of N consistent semiparametric regression estimator in Stata," Stata Journal, StataCorp LLC, vol. 12(4), pages 726-735, December.
    • Handle: RePEc:tsj:stataj:v:12:y:2012:i:4:p:736-735
    Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj12-4/st0278/
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    References listed on IDEAS

    as
    1. Michael Lokshin, 2006. "Difference-based semiparametric estimation of partial linear regression models," Stata Journal, StataCorp LLC, vol. 6(3), pages 377-383, September.
    2. Adonis Yatchew, 1998. "Nonparametric Regression Techniques in Economics," Journal of Economic Literature, American Economic Association, vol. 36(2), pages 669-721, June.
    3. Robinson, Peter M, 1988. "Root- N-Consistent Semiparametric Regression," Econometrica, Econometric Society, vol. 56(4), pages 931-954, July.
    Full references (including those not matched with items on IDEAS)

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    Keywords

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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