Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1145334 | Journal of Multivariate Analysis | 2016 | 26 Pages |
Abstract
We consider the asymptotic normality in L2 of kernel estimators of the long run covariance of stationary functional time series. Our results are established assuming a weakly dependent Bernoulli shift structure for the underlying observations, which contains most stationary functional time series models, under mild conditions. As a corollary, we obtain joint asymptotics for functional principal components computed from empirical long run covariance operators, showing that they have the favorable property of being asymptotically independent.
Keywords
Related Topics
Physical Sciences and Engineering
Mathematics
Numerical Analysis
Authors
István Berkes, Lajos Horváth, Gregory Rice,