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Cross-validating fit and predictive accuracy of nonlinear quantile regressions

Haupt, Harry and Kagerer, Kathrin and Schnurbus, Joachim (2011) Cross-validating fit and predictive accuracy of nonlinear quantile regressions. Journal of Applied Statistics 38 (12), pp. 2939-2954.

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Abstract

The paper proposes a cross-validation method to address the question of specification search in a multiple nonlinear quantile regression framework. Linear parametric, spline-based partially linear, and kernel-based fully nonparametric specifications are contrasted as competitors using cross-validated weighted L1-norm based goodness-of-fit and prediction error criteria. The aim is to provide a ...

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Item Type:Article
Date:December 2011
Institutions:Business, Economics and Information Systems > Institut für Volkswirtschaftslehre und Ökonometrie > Lehrstuhl für Ökonometrie (Prof. Dr. Rolf Tschernig)
Research groups and research centres:Not selected
Identification Number:
ValueType
10.1080/02664763.2011.573542DOI
Keywords:Quantile regression, spline, kernel, cross validation, model selection, mixed covariates
Subjects:300 Social sciences > 330 Economics
Status:Published
Refereed:Yes, this version has been refereed
Created at the University of Regensburg:Partially
Owner: Kathrin Kagerer
Deposited On:24 Jan 2012 09:31
Last Modified:22 Feb 2012 15:58
Item ID:23241
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