Factorisable multi-task quantile regression

Shih-Kang Chao, Wolfgang Härdle and Ming Yuan

No 2016-057, SFB 649 Discussion Papers from Humboldt University Berlin, Collaborative Research Center 649: Economic Risk

Abstract: For many applications, analyzing multiple response variables jointly is desirable because of their dependency, and valuable information about the distribution can be retrieved by estimating quantiles. In this paper, we propose a multi-task quantile regression method that exploits the potential factor structure of multivariate conditional quantiles through nuclear norm regularization. We jointly study the theoretical properties and computational aspects of the estimating procedure. In particular, we develop an efficient iterative proximal gradient algorithm for the non-smooth and non-strictly convex optimization problem incurred in our estimating procedure, and derive oracle bounds for the estimation error in a realistic situation where the sample size and number of iterative steps are both finite. The finite iteration analysis is particular useful when the matrix to be estimated is big and the computational cost is high. Merits of the proposed methodology are demonstrated through a Monte Carlo experiment and applications to climatological and financial study. Specifically, our method provides an objective foundation for spatial extreme clustering, and gives a refreshing look on the global financial systemic risk. Supplementary materials for this article are available online.

Keywords: Factor model; Fast iterative shrinkage-thresholding algorithm; Multivariate regression; Spatial extreme; Financial risk (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.econstor.eu/bitstream/10419/162499/1/87629347X.pdf (application/pdf)

Related works:
Journal Article: FACTORISABLE MULTITASK QUANTILE REGRESSION (2021)
Working Paper: Factorisable Multitask Quantile Regression (2020)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zbw:sfb649:sfb649dp2016-057

Access Statistics for this paper

More papers in SFB 649 Discussion Papers from Humboldt University Berlin, Collaborative Research Center 649: Economic Risk Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().