B0637
Title: Functional principal component analysis of cointegrated functional time series
Authors: Won-Ki Seo - University of Sydney (Australia) [presenting]
Abstract: Functional principal component analysis (FPCA) has played an important role in the development of functional time series analysis. The aim is to investigate how FPCA can be used to analyze cointegrated functional time series and proposes a modification of FPCA as a novel statistical tool. The modified FPCA not only provides an asymptotically more efficient estimator of the cointegrating vectors, but also leads to novel FPCA-based tests for examining some essential properties of cointegrated functional time series. As an empirical illustration, our methodology is applied to two empirical examples: U.S. age-specific employment rates and earning densities.
