Functional principal component analysis for cointegrated functional time series
Copyright policy )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 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.
- University of Sydney Australia
FOS: Computer and information sciences, functional principal component analysis, cointegration, Econometrics (econ.EM), unit roots, Mathematics - Statistics Theory, Statistics Theory (math.ST), Methodology (stat.ME), FOS: Economics and business, Inference from stochastic processes, FOS: Mathematics, 62M10, functional time series, Statistics - Methodology, Economics - Econometrics
FOS: Computer and information sciences, functional principal component analysis, cointegration, Econometrics (econ.EM), unit roots, Mathematics - Statistics Theory, Statistics Theory (math.ST), Methodology (stat.ME), FOS: Economics and business, Inference from stochastic processes, FOS: Mathematics, 62M10, functional time series, Statistics - Methodology, Economics - Econometrics
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