Bayesian variable selection in quantile regression models is often a difficult task due to the computational challenges …. However, an extreme quantile regression should have different regression coefficients from the median regression, and thus the … priors for quantile regression should depend on the quantile. In this article an extension of the Zellners prior which allows …

the impact over the earnings distribution using recently developed (instrumental) quantile regression method. Our results …

This note proposes a computationally simple estimator for quantile regression in a linear model context, as an …’s (1986) censored quantile regression estimator. …

We make use of quantile regression theory to obtain a combination of individual potentially-biased VaR forecasts that …

-sorted portfolios. Using dynamic quantile regression techniques, we report evidence of conditional tail predictability in terms of these …

alternative framework to decompose the dependence using quantile regression. We demonstrate that the methodology provides a …

The study intends to provide some updated empirical evidences on Israel education returns through quantile regression …