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. ISSN 0266-4763,
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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 L(1)-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 us 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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | SMOOTHING SPLINES; quantile regression; spline; kernel; cross validation; model selection; mixed covariates |
| Subjects: | 300 Social sciences > 330 Economics |
| Divisions: | Business, Economics and Information Systems > Institut für Volkswirtschaftslehre und Ökonometrie > Lehrstuhl für Ökonometrie (Prof. Dr. Rolf Tschernig) |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 01 Jul 2020 04:58 |
| Last Modified: | 01 Jul 2020 04:58 |
| URI: | https://pred.uni-regensburg.de/id/eprint/21603 |
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