g_cfar2: Generate a CFAR(2) Process

Description

Generate a convolutional functional autoregressive process with order 2.

Usage

g_cfar2(
  tmax = 1001,
  rho = 5,
  phi_func1 = NULL,
  phi_func2 = NULL,
  grid = 1000,
  sigma = 1,
  ini = 100
)
g_cfar2(
  tmax = 1001,
  rho = 5,
  phi_func1 = NULL,
  phi_func2 = NULL,
  grid = 1000,
  sigma = 1,
  ini = 100
)

Arguments

tmax

length of time.

rho

parameter for O-U process (noise process).

phi_func1

the first convolutional function. Default is 0.5*x^2+0.5*x+0.13.

phi_func2

the second convolutional function. Default is 0.7*x^4-0.1*x^3-0.15*x.

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:

cfar2

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

epsilon

the innovation at time tmax.

The function returns a list with components:

cfar2

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

Run this code

# NOT RUN {
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)
}
y=g_cfar2(100,5,phi_func1,phi_func2,grid=1000,sigma=1,ini=100)
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)
}
y=g_cfar2(1000,5,phi_func1,phi_func2,grid=1000,sigma=1,ini=100)
# }