NTS (version 1.1.1)

g_cfar1: Generate a CFAR(1) Process

Description

Generate a convolutional functional autoregressive process with order 1.

Generate a convolutional functional autoregressive process with order 1.

Usage

g_cfar1(
  tmax = 1001,
  rho = 5,
  phi_func = NULL,
  grid = 1000,
  sigma = 1,
  ini = 100
)
g_cfar1(
  tmax = 1001,
  rho = 5,
  phi_func = NULL,
  grid = 1000,
  sigma = 1,
  ini = 100
)

Arguments

tmax

length of time.

rho

parameter for O-U process (noise process).

phi_func

convolutional function. Default is 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:

cfar1

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

epsilon

the innovation at time tmax.

The function returns a list with components:

cfar1

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.

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

Examples

# NOT RUN {
phi_func= function(x)
{
 	return(dnorm(x,mean=0,sd=0.1))
}
y=g_cfar1(100,5,phi_func,grid=1000,sigma=1,ini=100)
phi_func= function(x)
{
 	return(dnorm(x,mean=0,sd=0.1))
}
y=g_cfar1(1000,5,phi_func,grid=1000,sigma=1,ini=100)
# }