Nonparametric identification in panels using quantiles

Nonparametric estimation of conditional quantiles using quantile regression trees

A nonparametric regression method that blends key features of piecewise polynomial quantile regression and tree-structured regression based on adaptive recursive partitioning of the covariate space is investigated. Unlike least squares regression trees, which concentrate on modeling the relationship between the response and the covariates at the center of the response distribution, our quantile...

Nonparametric Estimation of Conditional Quantiles Using Neural Networks

Nonparametric Estimation of Extreme Conditional Quantiles

The estimation of extreme conditional quantiles is an important issue in different scientific disciplines. Up to now, the extreme value literature focused mainly on estimation procedures based on i.i.d. samples. On the other hand, quantile regression based procedures work well for estimation within the data range i.e. the estimation of nonextreme quantiles but break down when main interest is i...

Noninformative Nonparametric Bayesian Estimation of Quantiles

In 1981 Rubin introduced the Bayesian bootstrap and argued that it was the natural Bayesian analogue to the usual bootstrap. We show here that when estimating a population quantile in a nonparametric problem it yields estimators that are often preferred to the natural naive estimators based on the order statistic. AMS 1980 Subject Classification: 62G05, 62C15 and 62G30

Nonparametric Prewhitening Estimators for Conditional Quantiles

We define a nonparametric prewhitening method for estimating conditional quantiles based on local linear quantile regression. We characterize the bias, variance and asymptotic normality of the proposed estimator. Under weak conditions our estimator can achieve bias reduction and have the same variance as the local linear quantile estimators. A small set of Monte Carlo simulations is carried out...