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Conditionally Parametric Quantile Regression

In: Quantile Regression for Spatial Data

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  • Daniel P. McMillen

    (University of Illinois)

Abstract

Chapter 2 demonstrated that nonparametric approaches can easily be adapted to quantile regression models. In the case of a single explanatory variable, x, all that is necessary to make the model nonparametric is to add a kernel weight function $$ k\left( {\left( {x - x_{t} } \right)/h} \right) $$ when estimating a quantile regression for a target point $$ x_{t} $$ . After estimating the function for a series of target points, the estimates can then be interpolated to all values of x. The nonparametric approach is a flexible way to add nonlinearity to the estimated quantile regressions.

Suggested Citation

  • Daniel P. McMillen, 2013. "Conditionally Parametric Quantile Regression," SpringerBriefs in Regional Science, in: Quantile Regression for Spatial Data, edition 127, chapter 0, pages 49-60, Springer.
    • Handle: RePEc:spr:sbrchp:978-3-642-31815-3_5
    DOI: 10.1007/978-3-642-31815-3_5
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