Conditionally Parametric Quantile Regression

Daniel McMillen ()

Chapter Chapter 5 in Quantile Regression for Spatial Data, 2013, pp 49-60 from Springer

Abstract: 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.

Keywords: Quantile Regression; Kernel Weighting Function; Ratio Assessment; Locfit; Downtown Chicago (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/978-3-642-31815-3_5

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