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Nonparametric estimation of an additive quantile regression model

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  • Joel L. Horowitz
  • Sokbae (Simon) Lee

Abstract

This paper is concerned with estimating the additive components of a nonparametric additive quantile regression model. We develop an estimator that is asymptotically normally distributed with a rate of convergence in probability of n-r/(2r+1) when the additive components are r-times continuously differentiable for some r ≥ 2. This result holds regardless of the dimension of the covariates and, therefore, the new estimator has no curse of dimensionality. In addition, the estimator has an oracle property and is easily extended to a generalized additive quantile regression model with a link function.The numerical performance and usefulness of the estimator are illustrated by Monte Carlo experiments and an empirical example.

Suggested Citation

  • Joel L. Horowitz & Sokbae (Simon) Lee, 2004. "Nonparametric estimation of an additive quantile regression model," CeMMAP working papers 07/04, Institute for Fiscal Studies.
    • Handle: RePEc:azt:cemmap:07/04
    DOI: 10.1920/wp.cem.2004.0704
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    1. Horowitz, Joel L. & Mammen, Enno, 2002. "Nonparametric estimation of an additive model with a link function," SFB 373 Discussion Papers 2002,63, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    2. Lee, Sokbae, 2003. "Efficient Semiparametric Estimation Of A Partially Linear Quantile Regression Model," Econometric Theory, Cambridge University Press, vol. 19(1), pages 1-31, February.
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    More about this item

    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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