methods are based on the sample forecast errors. In this paper we apply nonparametric quantile regression to the empirical …

nonparametric mean regression various meth- ods for bandwidth selection exists. But in nonparametric quantile regression band- width … local linear quantile regression based on cross-validation and local likeli- hood estimation is used. …

methods are based on the sample forecast errors. In this paper we apply nonparametric quantile regression to the empirical …

nonparametric mean regression various methods for bandwidth selection exists. But in nonparametric quantile regression bandwidth … local linear quantile regression based on cross-validation and local likelihood estimation is used. …

in a local interval about the introduction of SIMPLES. We use an estimator that combines both quantile regression and the …

This paper studies the connections among quantile regression, the asymmetric Laplace distribution, maximum likelihood … parameters together with the associated “most probable” quantile. Similarly, this method can be seen as a penalized quantile … regression estimator, where the penalty is given by deviations from the median regression. We derive the asymptotic properties of …

We discuss quantile regression techniques as a robust and easy to implement alternative for estimating Farell technical … efficiency scores. The quantile regression approach estimates the production process for benchmark banks located at top … apply the SFA and the quantile regression approach to German bank data for three banking groups, commercial banks, savings …