g_cfar: Generate a CFAR Process

In NTS: Nonlinear Time Series Analysis

Generate a CFAR Process

Description

Generate a convolutional functional autoregressive process.

Usage

g_cfar(
  tmax = 1001,
  rho = 5,
  phi_list = NULL,
  grid = 1000,
  sigma = 1,
  ini = 100
)

Arguments

tmax	length of time.
rho	parameter for O-U process (noise process).
phi_list	the convolutional function(s). Default is the density function of normal distribution with mean 0 and standard deviation 0.1.
grid	the number of grid points used to construct the functional time series. Default is 1000.
sigma	the standard deviation of O-U process. Default is 1.
ini	the burn-in period.

Value

The function returns a list with components:

cfar	a tmax-by-(grid+1) matrix following a CFAR(p) process.
epsilon	the innovation at time tmax.

References

Liu, X., Xiao, H., and Chen, R. (2016) Convolutional autoregressive models for functional time series. Journal of Econometrics, 194, 263-282.

NTS documentation built on Sept. 25, 2023, 1:08 a.m.