ftsa: long_run_covariance_estimation – R documentation

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long_run_covariance_estimation

Estimating long-run covariance function for a functional time series

Description

Bandwidth estimation in the long-run covariance function for a functional time series, using different types of kernel function

Usage

long_run_covariance_estimation(dat, C0 = 3, H = 3)

Arguments

`dat`	A matrix of p by n, where p denotes the number of grid points and n denotes sample size
`C0`	A tuning parameter used in the adaptive bandwidth selection algorithm of Rice
`H`	A tuning parameter used in the adaptive bandwidth selection algorithm of Rice

Value

An estimated covariance function of size (p by p)

Author(s)

Han Lin Shang

References

L. Horvath, G. Rice and S. Whipple (2016) Adaptive bandwidth selection in the long run covariance estimation of functional time series, Computational Statistics and Data Analysis, 100, 676-693.

G. Rice and H. L. Shang (2017) A plug-in bandwidth selection procedure for long run covariance estimation with stationary functional time series, Journal of Time Series Analysis, 38(4), 591-609.

D. Li, P. M. Robinson and H. L. Shang (2018) Long-range dependent curve time series, Journal of the American Statistical Association: Theory and Methods, under revision.

Examples

dum = long_run_covariance_estimation(dat = ElNino_OISST_region_1and2$y[,1:5])

ftsa

Functional Time Series Analysis

v6.0

GPL-3

Authors

Rob Hyndman [aut] (<https://orcid.org/0000-0002-2140-5352>), Han Lin Shang [aut, cre, cph] (<https://orcid.org/0000-0003-1769-6430>)

Initial release

2020-11-29