Functional principal component analysis for cointegrated functional time series

Functional principal component analysis (FPCA) has played an important role in the development of functional time series analysis. This note investigates how FPCA can be used to analyze cointegrated functional time series and proposes a modification of FPCA as a novel statistical tool. Our modified...

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Bibliographic Details
Published in:	Journal of time series analysis Vol. 45; no. 2; pp. 320 - 330
Main Author:	Seo, Won‐Ki
Format:	Journal Article
Language:	English
Published:	Oxford, UK John Wiley & Sons, Ltd 01-03-2024 Blackwell Publishing Ltd
Subjects:	Cointegration Cointegration analysis functional principal component analysis functional time series Principal components analysis Time series Unit roots
Online Access:	Get full text
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Summary:	Functional principal component analysis (FPCA) has played an important role in the development of functional time series analysis. This note investigates how FPCA can be used to analyze cointegrated functional time series and proposes a modification of FPCA as a novel statistical tool. Our modified FPCA not only provides an asymptotically more efficient estimator of the cointegrating vectors, but also leads to novel FPCA‐based tests for examining essential properties of cointegrated functional time series.
ISSN:	0143-9782 1467-9892
DOI:	10.1111/jtsa.12707