Beran, J, Liu, H and Ghosh, S (2020) On aggregation of strongly dependent time series. Scandinavian Journal of Statistics, 47 (3). pp. 690-710. ISSN 0303-6898
Abstract
We consider cross‐sectional aggregation of time series with long‐range dependence. This question arises for instance from the statistical analysis of networks where aggregation is defined via routing matrices. Asymptotically, aggregation turns out to increase dependence substantially, transforming a hyperbolic decay of autocorrelations to a slowly varying rate. This effect has direct consequences for statistical inference. For instance, unusually slow rates of convergence for nonparametric trend estimators and nonstandard formulas for optimal bandwidths are obtained. The situation changes, when time‐dependent aggregation is applied. Suitably chosen time‐dependent aggregation schemes can preserve a hyperbolic rate or even eliminate autocorrelations completely.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Keywords: | aggregation; kernel smoothing; long‐range dependence; network; time series |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 03 Aug 2020 15:06 |
Last Modified: | 07 Sep 2020 15:05 |
Status: | Published |
Publisher: | Wiley |
Identification Number: | 10.1111/sjos.12421 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:163796 |