Generalized canonical correlation variables improved estimation in high dimensional seemingly unrelated regression models
Li Zhao and
Xingzhong Xu
Statistics & Probability Letters, 2017, vol. 126, issue C, 119-126
Abstract:
After defining generalized canonical correlation variable pairs, this study proposes a new estimator of regression coefficients in seemingly unrelated regression models. The properties of the estimator are also discussed. The results of simulations show that the proposed estimator outperforms the ordinary least squares estimator.
Keywords: Generalized canonical correlation variable; High dimensional seemingly unrelated regression model; Improved estimator; Zellner’s two-stage estimator (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167715217300937
Full text for ScienceDirect subscribers only
Related works:
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:eee:stapro:v:126:y:2017:i:c:p:119-126
Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01
DOI: 10.1016/j.spl.2017.02.037
Access Statistics for this article
Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul
More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().