long_run_covariance_estimation {ftsa} | R Documentation |
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.
See Also
Examples
dum = long_run_covariance_estimation(dat = ElNino_OISST_region_1and2$y[,1:5])