We propose to smooth the entire objective function rather than only the check function in a linear quantile regression context. We derive a uniform Bahadur-Kiefer representation for the resulting convolution-type kernel ...

This paper extends the Oaxaca-Blinder decomposition method to the quantile regression random-coefficients framework. Mean-based decompositions are obtained as the integration of the quantile regression decomposition process. ...

This paper develops a uniform test of linearity against threshold effects in the quantile regression framework. The test is based on the supremum of the Wald process over the space of quantile and threshold parameters. We ...

The effects of prenatal care utilization on birth weight (BW) may vary by unobserved fetal health endowments. This heterogeneity will be masked by estimating the effects at BW mean but can be evaluated by estimating the ...

This paper is concerned with evaluating value at risk estimates. It is well known that using only binary variables to do this sacrifices too much information. However, most of the specification tests (also called backtests) ...

In this paper, we develop a reduced form multivariate quantile model, using a directional quantile framework. The proposed model is the solution to a collection of directional quantile models for a fixed orthonormal basis, ...

In this paper, we compare four different Value-at-Risk (V aR) methodologies through Monte Carlo experiments. Our results indicate that the method based on quantile regression with ARCH effect dominates other methods that ...