In this paper, we construct a new class of estimators for conditional quantiles in possibly
misspecified nonlinear models with time series data. Proposed estimators belong to the …

This chapter focuses on the question of correcting quantile regression estimates for
nonrandom sample selection. It provides the empirical illustration in Huber and Melly, and …

Traditionally, assessing the accuracy of inference based on regression quantiles has relied
on the Bahadur representation. This provides an error of order n^-1/4 in normal …

This paper studies estimation and inference for linear quantile regression models with
generated regressors. We suggest a practical two-step estimation procedure, where the …

Francis Galton in a famous passage defending the charms of statistics" against its many
detractors, chided his statistical colleagues who limited their inquiries to Averages, and do …

Traditional methods of modelling returns and testing the Capital Asset Pricing Model
(CAPM) do so at the mean of the conditional distribution. Instead, we model returns and test …

We construct a generic confidence interval for a conditional quantile via the direct approach.
It avoids estimating the conditional density function of the dependent variable given the …

It is well known that conventional Wald-type inference in the context of quantile regression is
complicated by the need to construct estimates of the conditional densities of the response …

This article studies the estimation of conditional quantiles of counts. Given the discreteness
of the data, some smoothness must be artificially imposed on the problem. We show that it is …