g_cfar: Generate a CFAR Process

In ConvFuncTimeSeries/test_t: Nonlinear time series analysis

Description

Generate a convolutional functional autoregressive process.

Usage

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

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 constrcut the functional time series. Default is 1000.
sigma	the standard deviation of O-U process. Default is 1.

Value

The function returns a list with components:

cfar	a tmax-by-(grid+1) matrix following a CFAR(1) 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.

Examples

phi_func= function(x)   
{
return(dnorm(x,mean=0,sd=0.1))
}
test=g_cfar(1000,5,phi_func)

phi_func1= function(x){
return(0.5*x^2+0.5*x+0.13)
}
phi_func2= function(x){
return(0.7*x^4-0.1*x^3-0.15*x)
}
phi_list=list(phi_func1,phi_func2)
y=g_cfar(1000,5,phi_list)

ConvFuncTimeSeries/test_t documentation built on May 29, 2019, 1:39 p.m.