Moments of a Wishart Matrix
Grant Hillier () and
Raymond Kan ()
Additional contact information
Grant Hillier: CeMMAP, University of Southampton
Raymond Kan: University of Toronto
Journal of Quantitative Economics, 2021, vol. 19, issue 1, No 9, 162 pages
Abstract:
Abstract The paper discusses the moments of Wishart matrices, in both the central and noncentral cases. The first part of the paper shows that the expectation map has certain homogeneity and equivariance properties which impose considerable structure on the moments, hitherto unrecognised. The second part of the paper explains how the moments may be computed efficiently. The two parts of the paper are completely independent, but the computations produce precisely the algebraic structure predicted in the first part, as well as reproducing all previously known formulae. A number of examples are given for the more manageable cases.
Keywords: Wishart matrix; Higher order moments; Homogeneity; Equivariance (search for similar items in EconPapers)
JEL-codes: C01 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)
Downloads: (external link)
http://link.springer.com/10.1007/s40953-021-00267-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
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:spr:jqecon:v:19:y:2021:i:1:d:10.1007_s40953-021-00267-7
Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/40953
DOI: 10.1007/s40953-021-00267-7
Access Statistics for this article
Journal of Quantitative Economics is currently edited by Dilip Nachane and P.G. Babu
More articles in Journal of Quantitative Economics from Springer, The Indian Econometric Society (TIES) Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().