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Nonstandard Quantile-Regression Inference


  • Goh, S.C.
  • Knight, K.


It is well known that conventional Wald-type inference in the context of quantile regression is complicated by the need to construct estimates of the conditional densities of the response variables at the quantile of interest. This note explores the possibility of circumventing the need to construct conditional density estimates in this context with scale statistics that are explicitly inconsistent for the underlying conditional densities. This method of studentization leads conventional test statistics to have limiting distributions that are nonstandard but have the convenient feature of depending explicitly on the user’s choice of smoothing parameter. These limiting distributions depend on the distribution of the conditioning variables but can be straightforwardly approximated by resampling.

Suggested Citation

  • Goh, S.C. & Knight, K., 2009. "Nonstandard Quantile-Regression Inference," Econometric Theory, Cambridge University Press, vol. 25(05), pages 1415-1432, October.
  • Handle: RePEc:cup:etheor:v:25:y:2009:i:05:p:1415-1432_09

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    References listed on IDEAS

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    Cited by:

    1. Kaplan, David M., 2015. "Improved quantile inference via fixed-smoothing asymptotics and Edgeworth expansion," Journal of Econometrics, Elsevier, vol. 185(1), pages 20-32.
    2. Fan, Yanqin & Liu, Ruixuan, 2016. "A direct approach to inference in nonparametric and semiparametric quantile models," Journal of Econometrics, Elsevier, vol. 191(1), pages 196-216.
    3. Fernandes, Marcelo & Guerre, Emmanuel & Horta, Eduardo, 2017. "Smoothing quantile regressions," Textos para discussão 457, FGV/EESP - Escola de Economia de São Paulo, Getulio Vargas Foundation (Brazil).

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