Cross-validating fit and predictive accuracy of nonlinear quantile regressions

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 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.