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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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 fair comparison with respect to estimation accuracy and/or predictive ability for different semi- and nonparametric specification paradigms. This is challenging as the model dimension cannot be estimated for all competitors and the meta-parameters such as kernel bandwidths, spline knot numbers and polynomial degrees are difficult to compare. General issues of specification comparability and automated data-driven meta-parameter selection are discussed. The proposed method further allows to assess the balance between fit and model complexity. An extensive Monte-Carlo study and an application to a well known data set provide empirical illustration of the method.
|Institutions:||Business, Economics and Information Systems > Institut für Volkswirtschaftslehre und Ökonometrie > Lehrstuhl für Ökonometrie (Prof. Dr. Rolf Tschernig)|
|Interdisciplinary subject network:||Not selected|
|Keywords:||Quantile regression, spline, kernel, cross validation, model selection, mixed covariates|
|Subjects:||300 Social sciences > 330 Economics|
|Refereed:||Yes, this version has been refereed|
|Created at the University of Regensburg:||Partially|
|Deposited On:||24 Jan 2012 10:31|
|Last Modified:||22 Feb 2012 16:58|
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