est_cfar: Estimation of a CFAR Process

In NTS: Nonlinear Time Series Analysis

Estimation of a CFAR Process

Description

Estimation of a CFAR process.

Usage

est_cfar(f, p = 3, df_b = 10, grid = 1000)

Arguments

f	the functional time series.
p	the CFAR order.
df_b	the degrees of freedom for natural cubic splines. Default is 10.
grid	the number of gird points used to construct the functional time series and noise process. Default is 1000.

Value

The function returns a list with components:

phi_coef	the estimated spline coefficients for convolutional function values, a (2*grid+1)-by-p matrix.
phi_func	the estimated convolutional function(s), a (df_b+1)-by-p matrix.
rho	estimated rho for O-U process (noise process).
sigma	estimated sigma for O-U process (noise process).

References

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

NTS documentation built on Sept. 25, 2023, 1:08 a.m.