Finding hidden structure of sparse longitudinal data via functional Eigenfunctions

Jong-Min Kim and Sun Young Hwang

Applied Economics Letters, 2024, vol. 31, issue 12, 1142-1149

Abstract: In this research, we are interested in finding the hidden dependence structure of sparse longitudinal data. Finding the hidden dependence structure of sparse longitudinal data is difficult due to the starting and end times being different. We propose that finding the directional dependence structure of the eigenfunctions by sparse functional principal component analysis (FPCA) may be a good alternative solution to find the hidden dependence structure of sparse longitudinal data. To verify this idea, we apply sparse FPCA to simulated data and two real datasets, wage sparse longitudinal data and Korea composite stock price index (KOSPI) high-frequency minute tick data and then apply vine copula and copula dynamic conditional correlation with asymmetric GARCH model to the functional eigenfunctions from FPCA.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/13504851.2023.2176440 (text/html)
Access to full text is restricted to subscribers.

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:taf:apeclt:v:31:y:2024:i:12:p:1142-1149

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAEL20

DOI: 10.1080/13504851.2023.2176440

Access Statistics for this article

Applied Economics Letters is currently edited by Anita Phillips

More articles in Applied Economics Letters from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().