Vector quantile regression beyond correct specification

TL;DR

This paper advances the understanding of vector quantile regression (VQR) by demonstrating solution existence under misspecification and comparing it to classical methods, enriching the modeling of conditional distributions.

Contribution

It extends VQR theory beyond correct specification, showing solution existence and linking VQR to classical quantile regression with monotonicity constraints.

Findings

VQR solutions exist even under model misspecification.

VQR generalizes classical quantile regression with added monotonicity.

Comparison shows equivalence of VQR and classical quantile regression in univariate cases.

Abstract

This paper studies vector quantile regression (VQR), which is a way to model the dependence of a random vector of interest with respect to a vector of explanatory variables so to capture the whole conditional distribution, and not only the conditional mean. The problem of vector quantile regression is formulated as an optimal transport problem subject to an additional mean-independence condition. This paper provides a new set of results on VQR beyond the case with correct specification which had been the focus of previous work. First, we show that even under misspecification, the VQR problem still has a solution which provides a general representation of the conditional dependence between random vectors. Second, we provide a detailed comparison with the classical approach of Koenker and Bassett in the case when the dependent variable is univariate and we show that in that case, VQR is equivalent to classical quantile regression with an additional monotonicity constraint.