On the asymptotic normality of kernel estimators of the long run covariance of functional time series

István Berkes, Lajos Horváth, Gregory Rice

Research output: Contribution to journalArticlepeer-review

Abstract

We consider the asymptotic normality in 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.
Original languageEnglish
Pages (from-to)150-175
JournalJournal of Multivariate Analysis
Volume144
DOIs
Publication statusPublished - 2016

Fields of Expertise

  • Information, Communication & Computing

Treatment code (Nähere Zuordnung)

  • Basic - Fundamental (Grundlagenforschung)

Fingerprint

Dive into the research topics of 'On the asymptotic normality of kernel estimators of the long run covariance of functional time series'. Together they form a unique fingerprint.

Cite this