Generate a convolutional functional autoregressive process.
g_cfar(
tmax = 1001,
rho = 5,
phi_list = NULL,
grid = 1000,
sigma = 1,
ini = 100
)The function returns a list with components:
a tmax-by-(grid+1) matrix following a CFAR(p) process.
the innovation at time tmax.
length of time.
parameter for O-U process (noise process).
the convolutional function(s). Default is the density function of normal distribution with mean 0 and standard deviation 0.1.
the number of grid points used to construct the functional time series. Default is 1000.
the standard deviation of O-U process. Default is 1.
the burn-in period.
Liu, X., Xiao, H., and Chen, R. (2016) Convolutional autoregressive models for functional time series. Journal of Econometrics, 194, 263-282.