A Grey Structure Incidence Clustering Method for Panel Data and its Application
Shi-tong Liu (),
Yong Liu () and
Yue Lu ()
Additional contact information
Shi-tong Liu: Jiangnan University, School of Business
Yong Liu: Jiangnan University, School of Business
Yue Lu: Jiangnan University, School of Business
Computational Economics, 2025, vol. 66, issue 6, No 4, 4559-4588
Abstract:
Abstract Panel data can well describe and depict the systemic and dynamic of the research objects. However, it is difficult for traditional panel data analysis methods to accurately reflect the structural information of the research object and scientifically deal with the multi-attribute decision problem of presenting the similarity of systemic structure among the research objects. In view of this, with respect to the clustering problems for panel data, considering system structural characteristics of the research objects such as scale volume, component weight, development trend and volatility, by using theories and methods such as GM(1,1) and grey incidence clustering method, we construct a grey structure incidence clustering method for panel data, and exploit it to deal with the clustering problems of the innovation capability of high-tech industries in China.
Keywords: Structure characteristics; Structural information; Grey structural incidence; Clustering method; Panel data (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
http://link.springer.com/10.1007/s10614-025-10850-2 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:kap:compec:v:66:y:2025:i:6:d:10.1007_s10614-025-10850-2
Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2
DOI: 10.1007/s10614-025-10850-2
Access Statistics for this article
Computational Economics is currently edited by Hans Amman
More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().