Joint estimation of conditional quantiles in multivariate linear regression models with an application to financial distress
Lea Petrella and
Journal of Multivariate Analysis, 2019, vol. 173, issue C, 70-84
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 studies 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 examines the main determinants of financial distress in a sample of Italian firms.
Keywords: EM algorithm; Maximum likelihood; Multivariate asymmetric Laplace distribution; Multiple quantiles; Multivariate response variables; Quantile regression (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:173:y:2019:i:c:p:70-84
Ordering information: This journal article can be ordered from
https://shop.elsevie ... _01_ooc_1&version=01
Access Statistics for this article
Journal of Multivariate Analysis is currently edited by de Leeuw, J.
More articles in Journal of Multivariate Analysis from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().