Joint estimation of conditional quantiles in multivariate linear regression models. An application to financial distress

TL;DR

This paper introduces a maximum-likelihood EM algorithm for jointly estimating multivariate conditional quantiles in linear regression, accounting for response associations and variable selection, with applications to financial distress analysis.

Contribution

It extends univariate quantile regression to multivariate responses using a reparameterized Multivariate Asymmetric Laplace distribution and develops a new EM algorithm for efficient estimation.

Findings

The proposed method outperforms separate univariate quantile regressions in efficiency.

Simulation studies validate the approach's accuracy and effectiveness.

Application to financial data identifies key determinants of distress.

Abstract

This paper proposes a maximum-likelihood approach to jointly estimate marginal conditional quantiles of multivariate response variables in a linear regression framework. We consider a slight reparameterization of the Multivariate Asymmetric Laplace distribution proposed by Kotz et al (2001) and exploit its location-scale mixture representation to implement a new EM algorithm for estimating model parameters. The idea is to extend the link between the Asymmetric Laplace distribution and the well-known univariate quantile regression model to a multivariate context, i.e. when a multivariate dependent variable is concerned. The approach accounts for association among multiple responses and study how the relationship between responses and explanatory variables can vary across different quantiles of the marginal conditional distribution of the responses. A penalized version of the EM algorithm is also presented to tackle the problem of variable selection. The validity of our approach is analyzed in a simulation study, where we also provide evidence on the efficiency gain of the proposed method compared to estimation obtained by separate univariate quantile regressions. A real data application is finally proposed to study the main determinants of financial distress in a sample of Italian firms.