FUNCTIONAL PRINCIPAL COMPONENT ANALYSIS FOR COINTEGRATED FUNCTIONAL TIME SERIES

被引：5
|
作者：
Seo, Won-Ki [1 ,2 ]
机构：
[1] Univ Sydney, Sch Econ, Sydney, Australia
[2] Univ Sydney, Level 5,Social Sci Bldg, Sydney, NSW 2006, Australia
来源：
JOURNAL OF TIME SERIES ANALYSIS | 2024年 / 45卷 / 02期
关键词：
Cointegration; functional principal component analysis; functional time series; unit roots; STATIONARITY; REGRESSION;
D O I：
10.1111/jtsa.12707
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
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.
引用
收藏
页码：320 / 330
页数：11
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