Description Usage Arguments Value References Examples
Estimation of a CFAR process.
1 |
f |
the functional time series. |
p |
CFAR order. |
df_b |
the degrees of freedom for natural cubic splines. Default is 10. |
grid |
the number of gird points used to constrct the functional time series and noise process. Default is 1000. |
The function returns a list with components:
phi_coef |
estimated spline coefficients for convluaional function(s). |
phi_func |
estimated convoluational function(s). |
rho |
estimated rho for O-U process (noise process). |
sigma |
estimated sigma for O-U process (noise process). |
Liu, X., Xiao, H., and Chen, R. (2016) Convolutional autoregressive models for functional time series. Journal of Econometrics, 194, 263-282.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | phi_func= function(x)
{
return(dnorm(x,mean=0,sd=0.1))
}
y=g_cfar1(1000,5,phi_func)
library(MASS)
library(splines)
f_grid=y$cfar
index=seq(1,1001,by=10)
f=f_grid[,index]
est=est_cfar(f,1)
b_grid=seq(-1,1,by=1/grid)
par(mfcol=c(1,1))
c1 <- range(est$phi_func)
plot(b_grid,phi_func(b_grid),type='l',col='red',ylim=c1*1.1)
points(b_grid,est$phi_func,type='l')
|
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